{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T19:05:39Z","timestamp":1777403139876,"version":"3.51.4"},"reference-count":289,"publisher":"Springer Science and Business Media LLC","issue":"S2","license":[{"start":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T00:00:00Z","timestamp":1691366400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T00:00:00Z","timestamp":1691366400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001773","name":"University of New South Wales","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001773","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"published-print":{"date-parts":[[2023,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Federated learning (FL) is a machine learning approach that decentralizes data and its processing by allowing clients to train intermediate models on their devices with locally stored data. It aims to preserve privacy as only model updates are shared with a central server rather than raw data. In recent years, many reviews have evaluated FL from the system (general challenges) or server\u2019s perspectives, ignoring the importance of clients\u2019 perspectives. Although FL helps users have control over their data, there are many challenges arising from decentralization, specifically from the perspectives of clients who are the main contributors to FL. Therefore, in response to the gap in the literature, this study intends to explore client-side challenges and available solutions by conducting a systematic literature review on 238 primary studies. Further, we analyze if a solution identified for one type of challenge is also applicable to other challenges and if there are impacts to consider. The conclusion of this survey reveals that servers and platforms have to work with clients to address client-side challenges.<\/jats:p>","DOI":"10.1007\/s10462-023-10563-8","type":"journal-article","created":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T02:01:46Z","timestamp":1691373706000},"page":"1773-1827","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["A systematic review of federated learning from clients\u2019 perspective: challenges and solutions"],"prefix":"10.1007","volume":"56","author":[{"given":"Yashothara","family":"Shanmugarasa","sequence":"first","affiliation":[]},{"given":"Hye-young","family":"Paik","sequence":"additional","affiliation":[]},{"given":"Salil S.","family":"Kanhere","sequence":"additional","affiliation":[]},{"given":"Liming","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,7]]},"reference":[{"issue":"6","key":"10563_CR1","doi-asserted-by":"crossref","first-page":"4723","DOI":"10.1109\/JIOT.2020.3028742","volume":"8","author":"S AbdulRahman","year":"2020","unstructured":"AbdulRahman S, Tout H, Mourad A et al (2020) FedMCCS: multicriteria client selection model for optimal IoT federated learning. IEEE Internet Things J 8(6):4723\u20134735","journal-title":"IEEE Internet Things J"},{"key":"10563_CR2","first-page":"8392","volume":"34","author":"I Achituve","year":"2021","unstructured":"Achituve I, Shamsian A, Navon A et al (2021) Personalized federated learning with gaussian processes. Adv Neural Inf Process Syst 34:8392\u20138406","journal-title":"Adv Neural Inf Process Syst"},{"key":"10563_CR3","doi-asserted-by":"crossref","unstructured":"Al-Abiad MS, Hassan M, Hossain M, et\u00a0al (2021) Energy efficient federated learning in integrated fog-cloud computing enabled internet-of-things networks. arXiv:2107.03520","DOI":"10.1109\/ICCWorkshops53468.2022.9814690"},{"key":"10563_CR4","doi-asserted-by":"crossref","unstructured":"Alazab M, RM SP, Parimala M, et\u00a0al (2021) Federated learning for cybersecurity: concepts, challenges and future directions. IEEE Trans Ind Inf 18:3501\u20133509","DOI":"10.1109\/TII.2021.3119038"},{"key":"10563_CR5","unstructured":"Aldaghri N, Mahdavifar H, Beirami A (2021) FeO2: federated learning with opt-out differential privacy. arXiv:2110.15252"},{"key":"10563_CR6","doi-asserted-by":"crossref","unstructured":"Aledhari M, Razzak R, Parizi RM, et\u00a0al (2020) Federated learning: a survey on enabling technologies, protocols, and applications. IEEE Access 8:140699\u2013140725","DOI":"10.1109\/ACCESS.2020.3013541"},{"key":"10563_CR7","doi-asserted-by":"crossref","unstructured":"Alvi SA, Hong Y, Durrani S (2021) Utility fairness for the differentially private federated learning. arXiv:2109.05267","DOI":"10.1109\/JIOT.2022.3165596"},{"key":"10563_CR8","unstructured":"Amiri MM, Gunduz D, Kulkarni SR, et\u00a0al (2020) Federated learning with quantized global model updates. arXiv:2006.10672"},{"key":"10563_CR9","unstructured":"Arivazhagan MG, Aggarwal V, Singh AK, et\u00a0al (2019) Federated learning with personalization layers. arXiv:1912.00818"},{"issue":"8","key":"10563_CR10","doi-asserted-by":"crossref","first-page":"2864","DOI":"10.3390\/app10082864","volume":"10","author":"M Asad","year":"2020","unstructured":"Asad M, Moustafa A, Ito T (2020) Fedopt: Towards communication efficiency and privacy preservation in federated learning. Appl Sci 10(8):2864","journal-title":"Appl Sci"},{"key":"10563_CR11","unstructured":"Avdiukhin D, Kasiviswanathan S (2021) Federated learning under arbitrary communication patterns. In: International conference on machine learning. PMLR, pp 425\u2013435"},{"key":"10563_CR12","unstructured":"Bagdasaryan E, Veit A, Hua Y, et\u00a0al (2020) How to backdoor federated learning. In: International conference on artificial intelligence and statistics. PMLR, pp 2938\u20132948"},{"issue":"1","key":"10563_CR13","doi-asserted-by":"crossref","first-page":"17","DOI":"10.3390\/jsan10010017","volume":"10","author":"R Balakrishnan","year":"2021","unstructured":"Balakrishnan R, Akdeniz M, Dhakal S et al (2021) Resource management and model personalization for federated learning over wireless edge networks. J Sens Actuator Netw 10(1):17","journal-title":"J Sens Actuator Netw"},{"key":"10563_CR14","doi-asserted-by":"crossref","unstructured":"Bao X, Su C, Xiong Y, et\u00a0al (2019) Flchain: a blockchain for auditable federated learning with trust and incentive. In: 2019 5th international conference on big data computing and communications (BIGCOM). IEEE, pp 151\u2013159","DOI":"10.1109\/BIGCOM.2019.00030"},{"key":"10563_CR15","unstructured":"Bernstein J, Wang YX, Azizzadenesheli K, et\u00a0al (2018) signSGD: compressed optimisation for non-convex problems. In: International conference on machine learning. PMLR, pp 560\u2013569"},{"key":"10563_CR16","unstructured":"Bhagoji AN, Chakraborty S, Mittal P, et\u00a0al (2019) Analyzing federated learning through an adversarial lens. In: Chaudhuri K, Salakhutdinov R (eds) Proceedings of the 36th international conference on machine learning, proceedings of machine learning research, vol\u00a097. PMLR, pp 634\u2013643, https:\/\/proceedings.mlr.press\/v97\/bhagoji19a.html"},{"issue":"104","key":"10563_CR17","first-page":"468","volume":"106","author":"A Blanco-Justicia","year":"2021","unstructured":"Blanco-Justicia A, Domingo-Ferrer J, Mart\u00ednez S et al (2021) Achieving security and privacy in federated learning systems: survey, research challenges and future directions. Eng Appl Artif Intell 106(104):468","journal-title":"Eng Appl Artif Intell"},{"key":"10563_CR18","doi-asserted-by":"crossref","unstructured":"Bouacida N, Hou J, Zang H, et\u00a0al (2020) Adaptive federated dropout: improving communication efficiency and generalization for federated learning. arXiv:2011.04050","DOI":"10.1109\/INFOCOMWKSHPS51825.2021.9484526"},{"key":"10563_CR19","doi-asserted-by":"crossref","unstructured":"Briggs C, Fan Z, Andras P (2020) Federated learning with hierarchical clustering of local updates to improve training on non-iid data. In: 2020 international joint conference on neural networks (IJCNN). IEEE, pp 1\u20139","DOI":"10.1109\/IJCNN48605.2020.9207469"},{"key":"10563_CR20","doi-asserted-by":"crossref","unstructured":"Briggs C, Fan Z, Andras P (2021) A review of privacy-preserving federated learning for the internet-of-things. Fed Learn Syst pp 21\u201350","DOI":"10.1007\/978-3-030-70604-3_2"},{"key":"10563_CR21","unstructured":"Caldas S, Duddu SMK, Wu P, et\u00a0al (2018) Leaf: a benchmark for federated settings. arXiv:1812.01097"},{"key":"10563_CR22","doi-asserted-by":"crossref","unstructured":"Cao D, Chang S, Lin Z, et\u00a0al (2019) Understanding distributed poisoning attack in federated learning. In: 2019 IEEE 25th international conference on parallel and distributed systems (ICPADS). IEEE, pp 233\u2013239","DOI":"10.1109\/ICPADS47876.2019.00042"},{"key":"10563_CR23","unstructured":"Chai D, Wang L, Chen K, et\u00a0al (2020a) Fedeval: a benchmark system with a comprehensive evaluation model for federated learning. arXiv:2011.09655"},{"key":"10563_CR24","doi-asserted-by":"crossref","unstructured":"Chai Z, Chen Y, Zhao L, et\u00a0al (2020b) Fedat: a communication-efficient federated learning method with asynchronous tiers under non-iid data. ArXivorg","DOI":"10.1145\/3458817.3476211"},{"key":"10563_CR25","unstructured":"Chang WT, Tandon R (2020) Communication efficient federated learning over multiple access channels. arXiv:2001.08737"},{"key":"10563_CR26","unstructured":"Chen F, Luo M, Dong Z, et\u00a0al (2018) Federated meta-learning with fast convergence and efficient communication. arXiv:1802.07876"},{"key":"10563_CR27","unstructured":"Chen R, Li L, Xue K, et\u00a0al (2020a) To talk or to work: energy efficient federated learning over mobile devices via the weight quantization and 5G transmission co-design. arXiv:2012.11070"},{"key":"10563_CR28","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.ins.2020.02.037","volume":"522","author":"Y Chen","year":"2020","unstructured":"Chen Y, Luo F, Li T et al (2020b) A training-integrity privacy-preserving federated learning scheme with trusted execution environment. Inf Sci 522:69\u201379","journal-title":"Inf Sci"},{"issue":"4","key":"10563_CR29","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/MIS.2020.2988604","volume":"35","author":"Y Chen","year":"2020","unstructured":"Chen Y, Qin X, Wang J et al (2020c) Fedhealth: a federated transfer learning framework for wearable healthcare. IEEE Intell Syst 35(4):83\u201393","journal-title":"IEEE Intell Syst"},{"key":"10563_CR30","unstructured":"Chen Y, Li J, Wang F et\u00a0al (2021) DS2PM: a data sharing privacy protection model based on blockchain and federated learning. IEEE Internet of Things Journal"},{"key":"10563_CR31","unstructured":"Cheng G, Chadha K, Duchi J (2021) Fine-tuning is fine in federated learning. arXiv:2108.07313"},{"key":"10563_CR32","unstructured":"Cho YJ, Wang J, Joshi G (2020) Client selection in federated learning: convergence analysis and power-of-choice selection strategies. arXiv:2010.01243"},{"key":"10563_CR33","unstructured":"Cho YJ, Wang J, Chiruvolu T, et\u00a0al (2021) Personalized federated learning for heterogeneous clients with clustered knowledge transfer. arXiv:2109.08119"},{"key":"10563_CR34","doi-asserted-by":"crossref","unstructured":"Chou YH, Hong S, Sun C, et\u00a0al (2021) Grp-fed: Addressing client imbalance in federated learning via global-regularized personalization. arXiv:2108.13858","DOI":"10.1137\/1.9781611977172.51"},{"key":"10563_CR35","unstructured":"Choudhury O, Gkoulalas-Divanis A, Salonidis T, et\u00a0al (2019) Differential privacy-enabled federated learning for sensitive health data. arXiv:1910.02578"},{"key":"10563_CR36","unstructured":"Chu L, Wang L, Dong Y, et\u00a0al (2021) Fedfair: Training fair models in cross-silo federated learning. arXiv:2109.05662"},{"key":"10563_CR37","unstructured":"Collins L, Hassani H, Mokhtari A, et\u00a0al (2021) Exploiting shared representations for personalized federated learning. arXiv:2102.07078"},{"key":"10563_CR38","unstructured":"Cong M, Yu H, Weng X, et\u00a0al (2020) A VCG-based fair incentive mechanism for federated learning. arXiv:2008.06680"},{"key":"10563_CR39","unstructured":"Dai X, Yan X, Zhou K, et\u00a0al (2019) Hyper-sphere quantization: communication-efficient sgd for federated learning. arXiv:1911.04655"},{"issue":"1","key":"10563_CR40","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s00766-010-0115-7","volume":"16","author":"M Deng","year":"2011","unstructured":"Deng M, Wuyts K, Scandariato R et al (2011) A privacy threat analysis framework: supporting the elicitation and fulfillment of privacy requirements. Requirements Eng 16(1):3\u201332","journal-title":"Requirements Eng"},{"key":"10563_CR41","unstructured":"Deng Y, Kamani MM, Mahdavi M (2020) Adaptive personalized federated learning. arXiv:2003.13461"},{"key":"10563_CR43","unstructured":"Ding N, Fang Z, Huang J (2020) Incentive mechanism design for federated learning with multi-dimensional private information. In: 2020 18th international symposium on modeling and optimization in mobile, ad hoc, and wireless networks (WiOPT), pp 1\u20138"},{"key":"10563_CR42","first-page":"8752","volume-title":"ICASSP 2022\u20132022 IEEE International Conference on Acoustics","author":"J Ding","year":"2022","unstructured":"Ding J, Tramel E, Sahu AK et al (2022) Federated learning challenges and opportunities: an outlook. In: ICASSP 2022\u20132022 IEEE international conference on acoustics. Speech and signal processing (ICASSP). IEEE, pp 8752\u20138756"},{"key":"10563_CR44","unstructured":"Dinh CT, Tran NH, Nguyen TD (2020) Personalized federated learning with moreau envelopes. arXiv:2006.08848"},{"key":"10563_CR45","unstructured":"Divi S, Farrukh H, Celik B (2021a) Unifying distillation with personalization in federated learning. arXiv:2105.15191"},{"key":"10563_CR46","unstructured":"Divi S, Lin YS, Farrukh H, et\u00a0al (2021b) New metrics to evaluate the performance and fairness of personalized federated learning. arXiv:2107.13173"},{"key":"10563_CR47","doi-asserted-by":"crossref","unstructured":"Do QV, Pham QV, Hwang WJ (2021) Deep reinforcement learning for energy-efficient federated learning in uav-enabled wireless powered networks. IEEE Commun Lett 26:99\u2013103","DOI":"10.1109\/LCOMM.2021.3122129"},{"key":"10563_CR48","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1109\/OJCS.2020.2992630","volume":"1","author":"Z Du","year":"2020","unstructured":"Du Z, Wu C, Yoshinaga T et al (2020) Federated learning for vehicular internet of things: Recent advances and open issues. IEEE Open J Comput Soc 1:45\u201361","journal-title":"IEEE Open Journal of the Computer Society"},{"issue":"1","key":"10563_CR49","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1109\/TPDS.2020.3009406","volume":"32","author":"M Duan","year":"2020","unstructured":"Duan M, Liu D, Chen X et al (2020) Self-balancing federated learning with global imbalanced data in mobile systems. IEEE Trans Parallel Distrib Syst 32(1):59\u201371","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"10563_CR50","doi-asserted-by":"crossref","unstructured":"Duan M, Liu D, Ji X, et\u00a0al (2021) Fedgroup: efficient federated learning via decomposed similarity-based clustering. In: 2021 IEEE international conference on parallel and distributed processing with applications, big data and cloud computing, sustainable computing and communications, social computing and networking (ISPA\/BDCloud\/SocialCom\/SustainCom). IEEE, pp 228\u2013237","DOI":"10.1109\/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00042"},{"key":"10563_CR51","doi-asserted-by":"crossref","unstructured":"Dwork C (2009) The differential privacy frontier. In: Theory of cryptography conference. Springer, New York, pp 496\u2013502","DOI":"10.1007\/978-3-642-00457-5_29"},{"key":"10563_CR52","doi-asserted-by":"crossref","unstructured":"Enthoven D, Al-Ars Z (2021) An overview of federated deep learning privacy attacks and defensive strategies. Federated Learn Syst pp 173\u2013196","DOI":"10.1007\/978-3-030-70604-3_8"},{"key":"10563_CR53","unstructured":"Ezzeldin YH, Yan S, He C, et\u00a0al (2021) Fairfed: enabling group fairness in federated learning. arXiv:2110.00857"},{"key":"10563_CR54","unstructured":"Fallah A, Mokhtari A, Ozdaglar A (2020) Personalized federated learning: a meta-learning approach. arXiv:2002.07948"},{"key":"10563_CR56","unstructured":"Fan T (2018) FATE-Board: FATE\u2019s visualization toolkit. https:\/\/github.com\/FederatedAI\/FATE-Board"},{"issue":"4","key":"10563_CR55","doi-asserted-by":"crossref","first-page":"2252","DOI":"10.1109\/JIOT.2020.3028101","volume":"8","author":"S Fan","year":"2020","unstructured":"Fan S, Zhang H, Zeng Y et al (2020) Hybrid blockchain-based resource trading system for federated learning in edge computing. IEEE Internet Things J 8(4):2252\u20132264","journal-title":"IEEE Internet Things J"},{"key":"10563_CR57","doi-asserted-by":"crossref","unstructured":"Fan Z, Fang H, Zhou Z, et\u00a0al (2021) Improving fairness for data valuation in federated learning. arXiv:2109.09046","DOI":"10.1109\/ICDE53745.2022.00228"},{"issue":"4","key":"10563_CR59","doi-asserted-by":"crossref","first-page":"94","DOI":"10.3390\/fi13040094","volume":"13","author":"H Fang","year":"2021","unstructured":"Fang H, Qian Q (2021) Privacy preserving machine learning with homomorphic encryption and federated learning. Future Internet 13(4):94","journal-title":"Future Internet"},{"key":"10563_CR60","unstructured":"Fang M, Cao X, Jia J, et\u00a0al (2020) Local model poisoning attacks to Byzantine-Robust federated learning. In: 29th USENIX security symposium (USENIX Security 20), pp 1605\u20131622"},{"key":"10563_CR58","doi-asserted-by":"crossref","unstructured":"Fang C, Guo Y, Ma J, et\u00a0al (2022) A privacy-preserving and verifiable federated learning method based on blockchain. Computer Commun 186:1\u201311","DOI":"10.1016\/j.comcom.2022.01.002"},{"key":"10563_CR61","unstructured":"Finn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: International conference on machine learning. PMLR, pp 1126\u20131135"},{"key":"10563_CR62","unstructured":"Fung C, Yoon CJ, Beschastnikh I (2018) Mitigating sybils in federated learning poisoning. arXiv:1808.04866"},{"key":"10563_CR63","doi-asserted-by":"crossref","unstructured":"Gao L, Li L, Chen Y, et\u00a0al (2021) FIFL: a fair incentive mechanism for federated learning. In: 50th international conference on parallel processing, pp 1\u201310","DOI":"10.1145\/3472456.3472469"},{"key":"10563_CR64","doi-asserted-by":"crossref","unstructured":"Garg P, Villasenor J, Foggo V (2020) Fairness metrics: a comparative analysis. In: 2020 IEEE international conference on big data (Big Data). IEEE, pp 3662\u20133666","DOI":"10.1109\/BigData50022.2020.9378025"},{"key":"10563_CR65","unstructured":"Geyer RC, Klein T, Nabi M (2017) Differentially private federated learning: a client level perspective. arXiv:1712.07557"},{"key":"10563_CR66","unstructured":"Ghosh A, Chung J, Yin D, et\u00a0al (2020) An efficient framework for clustered federated learning. arXiv:2006.04088"},{"key":"10563_CR67","first-page":"110","volume":"78","author":"O Goldreich","year":"1998","unstructured":"Goldreich O (1998) Secure multi-party computation. Manuscript preliminary version 78:110","journal-title":"Secure multi-party computation. Manuscript Preliminary version"},{"key":"10563_CR68","unstructured":"Guha N, Talwalkar A, Smith V (2019) One-shot federated learning. arXiv:1902.11175"},{"key":"10563_CR69","unstructured":"Hamer J, Mohri M, Suresh AT (2020) Fedboost: a communication-efficient algorithm for federated learning. In: International conference on machine learning. PMLR, pp 3973\u20133983"},{"key":"10563_CR70","doi-asserted-by":"crossref","unstructured":"Han X, Yu H, Gu H (2019) Visual inspection with federated learning. In: International conference on image analysis and recognition. Springer, New York, pp 52\u201364","DOI":"10.1007\/978-3-030-27272-2_5"},{"key":"10563_CR71","unstructured":"Hanzely F, Richt\u00e1rik P (2020) Federated learning of a mixture of global and local models. arXiv:2002.05516"},{"issue":"10","key":"10563_CR72","doi-asserted-by":"crossref","first-page":"6532","DOI":"10.1109\/TII.2019.2945367","volume":"16","author":"M Hao","year":"2019","unstructured":"Hao M, Li H, Luo X et al (2019) Efficient and privacy-enhanced federated learning for industrial artificial intelligence. IEEE Trans Ind Inf 16(10):6532\u20136542","journal-title":"IEEE Trans Industr Inf"},{"key":"10563_CR73","doi-asserted-by":"crossref","unstructured":"Hao W, El-Khamy M, Lee J, et\u00a0al (2021) Towards fair federated learning with zero-shot data augmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 3310\u20133319","DOI":"10.1109\/CVPRW53098.2021.00369"},{"key":"10563_CR74","unstructured":"Hard A, Rao K, Mathews R, et\u00a0al (2018) Federated learning for mobile keyboard prediction. arXiv:1811.03604"},{"key":"10563_CR75","unstructured":"He C, Tan C, Tang H, et\u00a0al (2019) Central server free federated learning over single-sided trust social networks. arXiv:1910.04956"},{"key":"10563_CR77","doi-asserted-by":"crossref","unstructured":"Hu R, Gong Y, Guo Y (2020a) Federated learning with sparsification-amplified privacy and adaptive optimization. arXiv:2008.01558","DOI":"10.24963\/ijcai.2021\/202"},{"issue":"10","key":"10563_CR78","doi-asserted-by":"crossref","first-page":"9530","DOI":"10.1109\/JIOT.2020.2991416","volume":"7","author":"R Hu","year":"2020","unstructured":"Hu R, Guo Y, Li H et al (2020b) Personalized federated learning with differential privacy. IEEE Internet Things J 7(10):9530\u20139539","journal-title":"IEEE Internet Things J"},{"key":"10563_CR76","doi-asserted-by":"publisher","unstructured":"Hu H, Salcic Z, Sun L, et\u00a0al (2021) Source inference attacks in federated learning. https:\/\/doi.org\/10.48550\/ARXIV.2109.05659,https:\/\/arxiv.org\/abs\/2109.05659","DOI":"10.48550\/ARXIV.2109.05659,"},{"issue":"103","key":"10563_CR79","first-page":"291","volume":"99","author":"L Huang","year":"2019","unstructured":"Huang L, Shea AL, Qian H et al (2019) Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records. J Biomed Inform 99(103):291","journal-title":"J Biomed Inform"},{"issue":"7","key":"10563_CR80","first-page":"1552","volume":"32","author":"T Huang","year":"2020","unstructured":"Huang T, Lin W, Wu W et al (2020a) An efficiency-boosting client selection scheme for federated learning with fairness guarantee. IEEE Trans Parallel Distrib Syst 32(7):1552\u20131564","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"10563_CR81","unstructured":"Huang W, Li T, Wang D, et\u00a0al (2020b) Fairness and accuracy in federated learning. arXiv:2012.10069"},{"issue":"1","key":"10563_CR82","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JIOT.2021.3095077","volume":"9","author":"A Imteaj","year":"2021","unstructured":"Imteaj A, Thakker U, Wang S et al (2021) A survey on federated learning for resource-constrained iot devices. IEEE Internet Things J 9(1):1\u201324","journal-title":"IEEE Internet Things J"},{"key":"10563_CR83","unstructured":"Jeong E, Oh S, Kim H, et\u00a0al (2018) Communication-efficient on-device machine learning: federated distillation and augmentation under non-iid private data. arXiv:1811.11479"},{"key":"10563_CR84","unstructured":"Ji S, Jiang W, Walid A, et\u00a0al (2020) Dynamic sampling and selective masking for communication-efficient federated learning. arXiv:2003.09603"},{"issue":"9","key":"10563_CR85","doi-asserted-by":"crossref","first-page":"9330","DOI":"10.1109\/TVT.2021.3098022","volume":"70","author":"Z Ji","year":"2021","unstructured":"Ji Z, Chen L, Zhao N et al (2021) Computation offloading for edge-assisted federated learning. IEEE Trans Veh Technol 70(9):9330\u20139344","journal-title":"IEEE Trans Veh Technol"},{"key":"10563_CR87","unstructured":"Jiang Y, Kone\u010dn\u1ef3 J, Rush K, et\u00a0al (2019a) Improving federated learning personalization via model agnostic meta learning. arXiv:1909.12488"},{"key":"10563_CR88","unstructured":"Jiang Y, Wang S, Valls V, et\u00a0al (2019b) Model pruning enables efficient federated learning on edge devices. arXiv:1909.12326"},{"key":"10563_CR86","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.ins.2021.05.077","volume":"576","author":"C Jiang","year":"2021","unstructured":"Jiang C, Xu C, Zhang Y (2021) PFLM: privacy-preserving federated learning with membership proof. Inf Sci 576:288\u2013311","journal-title":"Inf Sci"},{"key":"10563_CR89","doi-asserted-by":"publisher","unstructured":"Jiang Y, Wang S, Valls V, et\u00a0al (2022) Model pruning enables efficient federated learning on edge devices. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2022.3166101","DOI":"10.1109\/TNNLS.2022.3166101"},{"key":"10563_CR90","doi-asserted-by":"crossref","unstructured":"Jiao Y, Wang P, Niyato D, et\u00a0al (2020) Toward an automated auction framework for wireless federated learning services market. IEEE Trans Mob Comput 20:3034\u20133048","DOI":"10.1109\/TMC.2020.2994639"},{"key":"10563_CR91","doi-asserted-by":"crossref","unstructured":"Jourdan T, Boutet A, Frindel C (2021) Privacy assessment of federated learning using private personalized layers. arXiv:2106.08060","DOI":"10.1109\/MLSP52302.2021.9596237"},{"key":"10563_CR92","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1613\/jair.301","volume":"4","author":"LP Kaelbling","year":"1996","unstructured":"Kaelbling LP, Littman ML, Moore AW (1996) Reinforcement learning: a survey. J Artif Intell Res 4:237\u2013285","journal-title":"Journal of artificial intelligence research"},{"issue":"1\u20132","key":"10563_CR93","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000083","volume":"14","author":"P Kairouz","year":"2021","unstructured":"Kairouz P, McMahan HB, Avent B et al (2021) Advances and open problems in federated learning. Found Trends\u00ae Mach Learn 14(1\u20132):1\u2013210","journal-title":"Foundations and Trends\u00ae in Machine Learning"},{"issue":"17","key":"10563_CR94","doi-asserted-by":"crossref","first-page":"2081","DOI":"10.3390\/electronics10172081","volume":"10","author":"D Kang","year":"2021","unstructured":"Kang D, Ahn CW (2021) Communication cost reduction with partial structure in federated learning. Electronics 10(17):2081","journal-title":"Electronics"},{"issue":"6","key":"10563_CR95","doi-asserted-by":"crossref","first-page":"10,700","DOI":"10.1109\/JIOT.2019.2940820","volume":"6","author":"J Kang","year":"2019","unstructured":"Kang J, Xiong Z, Niyato D et al (2019a) Incentive mechanism for reliable federated learning: a joint optimization approach to combining reputation and contract theory. IEEE Internet Things J 6(6):10,700-10,714","journal-title":"IEEE Internet Things J"},{"key":"10563_CR96","doi-asserted-by":"crossref","unstructured":"Kang J, Xiong Z, Niyato D, et\u00a0al (2019b) Incentive design for efficient federated learning in mobile networks: a contract theory approach. In: 2019 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS). IEEE, pp 1\u20135","DOI":"10.1109\/VTS-APWCS.2019.8851649"},{"issue":"2","key":"10563_CR97","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/MWC.001.1900119","volume":"27","author":"J Kang","year":"2020","unstructured":"Kang J, Xiong Z, Niyato D et al (2020) Reliable federated learning for mobile networks. IEEE Wirel Commun 27(2):72\u201380","journal-title":"IEEE Wirel Commun"},{"key":"10563_CR98","unstructured":"Katevas K, Bagdasaryan E, Waterman J, et\u00a0al (2020) Policy-based federated learning. arXiv:2003.06612"},{"key":"10563_CR99","doi-asserted-by":"publisher","unstructured":"Khalfoun B, Ben\u00a0Mokhtar S, Bouchenak S, et\u00a0al (2021) Eden: Enforcing location privacy through re-identification risk assessment: a federated learning approach. Proc ACM Interact Mob Wearable Ubiquitous Technol. https:\/\/doi.org\/10.1145\/3463502","DOI":"10.1145\/3463502"},{"key":"10563_CR100","unstructured":"Khodak M, Balcan MF, Talwalkar A (2019) Adaptive gradient-based meta-learning methods. arXiv:1906.02717"},{"key":"10563_CR101","doi-asserted-by":"crossref","unstructured":"Kim Y, Al\u00a0Hakim E, Haraldson J, et\u00a0al (2021) Dynamic clustering in federated learning. In: ICC 2021-IEEE international conference on communications. IEEE, pp 1\u20136","DOI":"10.1109\/ICC42927.2021.9500877"},{"key":"10563_CR102","unstructured":"Kone\u010dn\u1ef3 J, McMahan HB, Yu FX, et\u00a0al (2016) Federated learning: strategies for improving communication efficiency. arXiv:1610.05492"},{"key":"10563_CR103","doi-asserted-by":"crossref","unstructured":"Kontoudis GP, Stilwell DJ (2022) Fully decentralized, scalable gaussian processes for multi-agent federated learning. arXiv:2203.02865","DOI":"10.1109\/ICRA48506.2021.9561566"},{"key":"10563_CR104","doi-asserted-by":"crossref","unstructured":"Kulkarni V, Kulkarni M, Pant A (2020) Survey of personalization techniques for federated learning. In: 2020 fourth world conference on smart trends in systems, security and sustainability (WorldS4). IEEE, pp 794\u2013797","DOI":"10.1109\/WorldS450073.2020.9210355"},{"key":"10563_CR105","doi-asserted-by":"crossref","unstructured":"Kurupathi SR, Maass W (2020) Survey on federated learning towards privacy preserving ai. In: Proceedings of computer science & information technology (CSIT), pp 1\u201319","DOI":"10.5121\/csit.2020.101120"},{"key":"10563_CR106","doi-asserted-by":"publisher","unstructured":"Le THT, Tran NH, Tun YK, et\u00a0al (2020) Auction based incentive design for efficient federated learning in cellular wireless networks. In: 2020 IEEE wireless communications and networking conference (WCNC), pp 1\u20136. https:\/\/doi.org\/10.1109\/WCNC45663.2020.9120773","DOI":"10.1109\/WCNC45663.2020.9120773"},{"key":"10563_CR107","doi-asserted-by":"crossref","unstructured":"Le THT, Tran NH, Tun YK, et\u00a0al (2021) An incentive mechanism for federated learning in wireless cellular network: an auction approach. IEEE Trans Wirel Commun 20:4874\u20134887","DOI":"10.1109\/TWC.2021.3062708"},{"key":"10563_CR111","unstructured":"Li D, Wang J (2019) FedMD: heterogenous federated learning via model distillation. arXiv:1910.03581"},{"key":"10563_CR117","doi-asserted-by":"crossref","unstructured":"Li R, Ma F, Jiang W, et\u00a0al (2019a) Online federated multitask learning. In: 2019 IEEE international conference on big data (Big Data). IEEE, pp 215\u2013220","DOI":"10.1109\/BigData47090.2019.9006060"},{"key":"10563_CR119","unstructured":"Li T, Sanjabi M, Beirami A, et\u00a0al (2019b) Fair resource allocation in federated learning. arXiv:1905.10497"},{"key":"10563_CR108","unstructured":"Li A, Sun J, Wang B, et\u00a0al (2020a) Lotteryfl: personalized and communication-efficient federated learning with lottery ticket hypothesis on non-IID datasets. arXiv:2008.03371"},{"issue":"106","key":"10563_CR112","first-page":"854","volume":"149","author":"L Li","year":"2020","unstructured":"Li L, Fan Y, Tse M et al (2020b) A review of applications in federated learning. Comput Ind Engi 149(106):854","journal-title":"Computers & Industrial Engineering"},{"key":"10563_CR114","doi-asserted-by":"crossref","unstructured":"Li M, Chen Y, Wang Y et al (2020c) Efficient asynchronous vertical federated learning via gradient prediction and double-end sparse compression. In: 2020 16th international conference on control, automation, robotics and vision (ICARCV). IEEE, pp 291\u2013296","DOI":"10.1109\/ICARCV50220.2020.9305383"},{"key":"10563_CR118","doi-asserted-by":"crossref","unstructured":"Li S, Qi Q, Wang J, et\u00a0al (2020d) Ggs: general gradient sparsification for federated learning in edge computing. In: ICC 2020-2020 IEEE international conference on communications (ICC). IEEE, pp 1\u20137","DOI":"10.1109\/ICC40277.2020.9148987"},{"issue":"3","key":"10563_CR120","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","volume":"37","author":"T Li","year":"2020","unstructured":"Li T, Sahu AK, Talwalkar A et al (2020e) Federated learning: challenges, methods, and future directions. IEEE Signal Process Mag 37(3):50\u201360","journal-title":"IEEE Signal Process Mag"},{"key":"10563_CR121","first-page":"429","volume":"2","author":"T Li","year":"2020","unstructured":"Li T, Sahu AK, Zaheer M et al (2020f) Federated optimization in heterogeneous networks. Proce Mach Learn Syst 2:429\u2013450","journal-title":"Proceedings of Machine Learning and Systems"},{"key":"10563_CR109","doi-asserted-by":"crossref","unstructured":"Li A, Sun J, Zeng X, et\u00a0al (2021a) FedMASK: joint computation and communication-efficient personalized federated learning via heterogeneous masking. In: Proceedings of the 19th ACM conference on embedded networked sensor systems, pp 42\u201355","DOI":"10.1145\/3485730.3485929"},{"key":"10563_CR110","doi-asserted-by":"crossref","unstructured":"Li C, Li G, Varshney PK (2021b) Federated learning with soft clustering. IEEE Internet Things J","DOI":"10.1109\/JIOT.2021.3113927"},{"key":"10563_CR113","doi-asserted-by":"crossref","unstructured":"Li L, Shi D, Hou R, et\u00a0al (2021c) To talk or to work: flexible communication compression for energy efficient federated learning over heterogeneous mobile edge devices. In: IEEE INFOCOM 2021-IEEE conference on computer communications. IEEE, pp 1\u201310","DOI":"10.1109\/INFOCOM42981.2021.9488839"},{"key":"10563_CR115","doi-asserted-by":"crossref","unstructured":"Li Q, Wei X, Lin H, et\u00a0al (2021d) Inspecting the running process of horizontal federated learning via visual analytics. IEEE Trans Visual Comput Graph 28:4085\u20134100","DOI":"10.1109\/TVCG.2021.3074010"},{"key":"10563_CR116","doi-asserted-by":"publisher","unstructured":"Li Q, Wen Z, Wu Z, et\u00a0al (2021e) A survey on federated learning systems: vision, hype and reality for data privacy and protection. IEEE Trans Knowl Data Eng. https:\/\/doi.org\/10.1109\/TKDE.2021.3124599","DOI":"10.1109\/TKDE.2021.3124599"},{"key":"10563_CR122","unstructured":"Li T, Hu S, Beirami A, et\u00a0al (2021f) Ditto: fair and robust federated learning through personalization. In: International conference on machine learning. PMLR, pp 6357\u20136368"},{"key":"10563_CR123","unstructured":"Li X, Qu Z, Zhao S, et\u00a0al (2021g) Lomar: a local defense against poisoning attack on federated learning. IEEE Trans Depend Secure Comput"},{"key":"10563_CR124","unstructured":"Liang PP, Liu T, Ziyin L, et\u00a0al (2020) Think locally, act globally: federated learning with local and global representations. arXiv:2001.01523"},{"issue":"3","key":"10563_CR125","doi-asserted-by":"crossref","first-page":"2031","DOI":"10.1109\/COMST.2020.2986024","volume":"22","author":"WYB Lim","year":"2020","unstructured":"Lim WYB, Luong NC, Hoang DT et al (2020) Federated learning in mobile edge networks: a comprehensive survey. IEEE Commun Surveys Tutor 22(3):2031\u20132063","journal-title":"IEEE Communications Surveys & Tutorials"},{"key":"10563_CR126","doi-asserted-by":"crossref","unstructured":"Lim WYB, Huang J, Xiong Z, et\u00a0al (2021) Towards federated learning in uav-enabled internet of vehicles: a multi-dimensional contract-matching approach. IEEE Trans Intell Transp Syst 22:5140\u20135154","DOI":"10.1109\/TITS.2021.3056341"},{"key":"10563_CR127","unstructured":"Lin J, Du M, Liu J (2019) Free-riders in federated learning: attacks and defenses. arXiv:1911.12560"},{"key":"10563_CR131","unstructured":"Liu Y, Wei J (2020) Incentives for federated learning: a hypothesis elicitation approach. arXiv:2007.10596"},{"key":"10563_CR128","doi-asserted-by":"crossref","unstructured":"Liu K, Dolan-Gavitt B, Garg S (2018) Fine-pruning: Defending against backdooring attacks on deep neural networks. In: International symposium on research in attacks, intrusions, and defenses. Springer, New York, pp 273\u2013294","DOI":"10.1007\/978-3-030-00470-5_13"},{"key":"10563_CR132","doi-asserted-by":"crossref","unstructured":"Liu Y, Ai Z, Sun S, et\u00a0al (2020a) Fedcoin: a peer-to-peer payment system for federated learning. In: Federated learning. Springer, New York, pp 125\u2013138","DOI":"10.1007\/978-3-030-63076-8_9"},{"issue":"4","key":"10563_CR133","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/MWC.01.1900525","volume":"27","author":"Y Liu","year":"2020","unstructured":"Liu Y, Peng J, Kang J et al (2020b) A secure federated learning framework for 5G networks. IEEE Wirel Commun 27(4):24\u201331","journal-title":"IEEE Wirel Commun"},{"key":"10563_CR129","unstructured":"Liu L, Zhang J, Song S, et\u00a0al (2021a) Hierarchical quantized federated learning: convergence analysis and system design. arXiv:2103.14272"},{"key":"10563_CR130","doi-asserted-by":"crossref","unstructured":"Liu S, Yu J, Deng X, et\u00a0al (2021b) FedCPF: an efficient-communication federated learning approach for vehicular edge computing in 6G communication networks. IEEE Trans Intell Transp Syst","DOI":"10.1109\/TITS.2021.3099368"},{"key":"10563_CR134","unstructured":"Lo SK, Liu Y, Lu Q, et\u00a0al (2021a) Blockchain-based trustworthy federated learning architecture. arXiv:2108.06912"},{"issue":"5","key":"10563_CR135","first-page":"1","volume":"54","author":"SK Lo","year":"2021","unstructured":"Lo SK, Lu Q, Wang C et al (2021b) A systematic literature review on federated machine learning: from a software engineering perspective. ACM Comput Surveys 54(5):1\u201339","journal-title":"ACM Computing Surveys (CSUR)"},{"issue":"6","key":"10563_CR136","doi-asserted-by":"crossref","first-page":"4177","DOI":"10.1109\/TII.2019.2942190","volume":"16","author":"Y Lu","year":"2019","unstructured":"Lu Y, Huang X, Dai Y et al (2019) Blockchain and federated learning for privacy-preserved data sharing in industrial iot. IEEE Trans Industr Inf 16(6):4177\u20134186","journal-title":"IEEE Trans Industr Inf"},{"issue":"8","key":"10563_CR137","doi-asserted-by":"crossref","first-page":"5709","DOI":"10.1109\/TII.2020.3010798","volume":"17","author":"Y Lu","year":"2020","unstructured":"Lu Y, Huang X, Zhang K et al (2020) Communication-efficient federated learning for digital twin edge networks in industrial iot. IEEE Trans Industr Inf 17(8):5709\u20135718","journal-title":"IEEE Trans Industr Inf"},{"key":"10563_CR138","doi-asserted-by":"crossref","unstructured":"Luo J, Wu S (2021) Adapt to adaptation: learning personalization for cross-silo federated learning. arXiv:2110.08394","DOI":"10.24963\/ijcai.2022\/301"},{"key":"10563_CR139","doi-asserted-by":"crossref","unstructured":"Lyu L, Xu X, Wang Q, et\u00a0al (2020a) Collaborative fairness in federated learning. In: Federated learning. Springer, p 189\u2013204","DOI":"10.1007\/978-3-030-63076-8_14"},{"key":"10563_CR140","doi-asserted-by":"crossref","unstructured":"Lyu L, Yu H, Yang Q (2020b) Threats to federated learning: a survey. arXiv:2003.02133","DOI":"10.1007\/978-3-030-63076-8_1"},{"issue":"11","key":"10563_CR141","doi-asserted-by":"crossref","first-page":"2524","DOI":"10.1109\/TPDS.2020.2996273","volume":"31","author":"L Lyu","year":"2020","unstructured":"Lyu L, Yu J, Nandakumar K et al (2020c) Towards fair and privacy-preserving federated deep models. IEEE Trans Parallel Distrib Syst 31(11):2524\u20132541","journal-title":"IEEE Trans Parallel Distrib Syst"},{"issue":"4","key":"10563_CR142","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1109\/MNET.001.1900506","volume":"34","author":"C Ma","year":"2020","unstructured":"Ma C, Li J, Ding M et al (2020) On safeguarding privacy and security in the framework of federated learning. IEEE Network 34(4):242\u2013248","journal-title":"IEEE Network"},{"key":"10563_CR143","unstructured":"Ma Z, Lu Y, Li W, et\u00a0al (2021) Pfedatt: attention-based personalized federated learning on heterogeneous clients. In: Asian conference on machine learning. PMLR, pp 1253\u20131268"},{"key":"10563_CR144","doi-asserted-by":"crossref","unstructured":"Mahara SS, Bharath B, et\u00a0al (2021) Multi-task federated edge learning (mtfeel) in wireless networks. arXiv:2108.02517","DOI":"10.1109\/NCC55593.2022.9806778"},{"key":"10563_CR145","doi-asserted-by":"crossref","unstructured":"Majeed U, Hong CS (2019) FLCHIAN: lederated learning via MEC-enabled blockchain network. In: 2019 20th Asia-Pacific network operations and management symposium (APNOMS). IEEE, pp 1\u20134","DOI":"10.23919\/APNOMS.2019.8892848"},{"key":"10563_CR146","unstructured":"Malekijoo A, Fadaeieslam MJ, Malekijou H, et\u00a0al (2021) FEDZIP: a compression framework for communication-efficient federated learning. arXiv:2102.01593"},{"key":"10563_CR147","doi-asserted-by":"crossref","unstructured":"Manna A, Kasyap H, Tripathy S (2021) Moat: Model agnostic defense against targeted poisoning attacks in federated learning. In: International conference on information and communications security. Springer, New York, pp 38\u201355","DOI":"10.1007\/978-3-030-86890-1_3"},{"key":"10563_CR148","unstructured":"Mansour Y, Mohri M, Ro J, et\u00a0al (2020) Three approaches for personalization with applications to federated learning. arXiv:2002.10619"},{"key":"10563_CR149","unstructured":"Marathe VJ, Kanani P (2022) Subject granular differential privacy in federated learning. arXiv:2206.03617"},{"key":"10563_CR150","doi-asserted-by":"crossref","unstructured":"Martinez I, Francis S, Hafid AS (2019) Record and reward federated learning contributions with blockchain. In: 2019 International conference on cyber-enabled distributed computing and knowledge discovery (CyberC). IEEE, pp 50\u201357","DOI":"10.1109\/CyberC.2019.00018"},{"key":"10563_CR151","unstructured":"McMahan B, Moore E, Ramage D, et\u00a0al (2017) Communication-efficient learning of deep networks from decentralized data. In: Artificial intelligence and statistics. PMLR, pp 1273\u20131282"},{"key":"10563_CR152","doi-asserted-by":"crossref","unstructured":"Mestoukirdi M, Zecchin M, Gesbert D, et\u00a0al (2021) User-centric federated learning. arXiv:2110.09869","DOI":"10.1109\/GCWkshps52748.2021.9682003"},{"key":"10563_CR153","doi-asserted-by":"crossref","unstructured":"Michieli U, Ozay M (2021) Are all users treated fairly in federated learning systems? In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 2318\u20132322","DOI":"10.1109\/CVPRW53098.2021.00263"},{"key":"10563_CR154","unstructured":"Mike (2018) Federated learning: distributed machine learning with data locality and privacy. https:\/\/blog.fastforwardlabs.com\/2018\/11\/14\/federated-learning.html"},{"key":"10563_CR155","unstructured":"Mills J, Hu J, Min G (2020) Multi-task federated learning for personalised deep neural networks in edge computing. arXiv:2007.09236"},{"key":"10563_CR156","unstructured":"Mo F, Haddadi H (2019) Efficient and private federated learning using TEE. In: Proceedings of EuroSys Conference, Dresden, Germany"},{"key":"10563_CR157","unstructured":"Mohri M, Sivek G, Suresh AT (2019) Agnostic federated learning. In: International conference on machine learning. PMLR, pp 4615\u20134625"},{"key":"10563_CR158","doi-asserted-by":"crossref","unstructured":"Moon J, Kum S, Kim Y, et\u00a0al (2020) A decentralized ai data management system in federated learning. In: 2020 international conference on intelligent systems and computer vision (ISCV). IEEE, pp 1\u20134","DOI":"10.1109\/ISCV49265.2020.9204271"},{"key":"10563_CR159","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1016\/j.future.2020.10.007","volume":"115","author":"V Mothukuri","year":"2021","unstructured":"Mothukuri V, Parizi RM, Pouriyeh S et al (2021) A survey on security and privacy of federated learning. Futur Gener Comput Syst 115:619\u2013640","journal-title":"Futur Gener Comput Syst"},{"key":"10563_CR160","doi-asserted-by":"crossref","unstructured":"Mugunthan V, Peraire-Bueno A, Kagal L (2020a) PrivacyFL: a simulator for privacy-preserving and secure federated learning. In: Proceedings of the 29th ACM international conference on information and knowledge management, pp 3085\u20133092","DOI":"10.1145\/3340531.3412771"},{"key":"10563_CR161","doi-asserted-by":"crossref","unstructured":"Mugunthan V, Rahman R, Kagal L (2020b) Blockflow: an accountable and privacy-preserving solution for federated learning. arXiv:2007.03856","DOI":"10.1145\/3340531.3412771"},{"key":"10563_CR162","doi-asserted-by":"crossref","unstructured":"Nadiger C, Kumar A, Abdelhak S (2019) Federated reinforcement learning for fast personalization. In: 2019 IEEE second international conference on artificial intelligence and knowledge engineering (AIKE). IEEE, pp 123\u2013127","DOI":"10.1109\/AIKE.2019.00031"},{"key":"10563_CR163","doi-asserted-by":"crossref","unstructured":"Naseri AM, Lucia W, Youssef A (2022) Confidentiality attacks against encrypted control systems. Cyber-Physical Systems pp 1\u201320","DOI":"10.1080\/23335777.2022.2051209"},{"key":"10563_CR164","doi-asserted-by":"crossref","unstructured":"Ng KL, Chen Z, Liu Z, et\u00a0al (2021) A multi-player game for studying federated learning incentive schemes. In: Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence, pp 5279\u20135281","DOI":"10.24963\/ijcai.2020\/769"},{"issue":"1","key":"10563_CR165","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1109\/JSAC.2020.3036952","volume":"39","author":"HT Nguyen","year":"2020","unstructured":"Nguyen HT, Sehwag V, Hosseinalipour S et al (2020) Fast-convergent federated learning. IEEE J Sel Areas Commun 39(1):201\u2013218","journal-title":"IEEE J Sel Areas Commun"},{"issue":"6","key":"10563_CR166","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/MCOM.001.1900461","volume":"58","author":"S Niknam","year":"2020","unstructured":"Niknam S, Dhillon HS, Reed JH (2020) Federated learning for wireless communications: motivation, opportunities, and challenges. IEEE Commun Mag 58(6):46\u201351","journal-title":"IEEE Commun Mag"},{"key":"10563_CR167","doi-asserted-by":"crossref","unstructured":"Nishio T, Yonetani R (2019) Client selection for federated learning with heterogeneous resources in mobile edge. In: ICC 2019-2019 IEEE international conference on communications (ICC). IEEE, pp 1\u20137","DOI":"10.1109\/ICC.2019.8761315"},{"key":"10563_CR168","doi-asserted-by":"crossref","unstructured":"Nishio T, Shinkuma R, Mandayam NB (2020) Estimation of individual device contributions for incentivizing federated learning. In: 2020 IEEE Globecom workshops (GC Wkshps. IEEE, pp 1\u20136","DOI":"10.1109\/GCWkshps50303.2020.9367484"},{"issue":"8","key":"10563_CR169","doi-asserted-by":"crossref","first-page":"5168","DOI":"10.1109\/TCOMM.2021.3083316","volume":"69","author":"MK Nori","year":"2021","unstructured":"Nori MK, Yun S, Kim IM (2021) Fast federated learning by balancing communication trade-offs. IEEE Trans Commun 69(8):5168\u20135182","journal-title":"IEEE Trans Commun"},{"key":"10563_CR170","doi-asserted-by":"crossref","unstructured":"Nour B, Cherkaoui S, Mlika Z (2021) Federated learning and proactive computation reuse at the edge of smart homes. IEEE Trans Netw Sci Eng 9:3045\u20133056","DOI":"10.1109\/TNSE.2021.3131246"},{"key":"10563_CR171","unstructured":"Orekondy T, Oh SJ, Zhang Y, et\u00a0al (2018) Gradient-leaks: understanding and controlling deanonymization in federated learning. arXiv:1805.05838"},{"key":"10563_CR172","unstructured":"Ozkara K, Singh N, Data D, et\u00a0al (2021) QuPeD: Quantized personalization via distillation with applications to federated learning. Adv Neural Inf Process Syst 34"},{"key":"10563_CR173","doi-asserted-by":"crossref","unstructured":"Padala M, Damle S, Gujar S (2021) Federated learning meets fairness and differential privacy. In: International conference on neural information processing. Springer, New York, pp 692\u2013699","DOI":"10.1007\/978-3-030-92310-5_80"},{"key":"10563_CR174","doi-asserted-by":"publisher","unstructured":"Page MJ, McKenzie JE, Bossuyt PM, et\u00a0al (2021) The prisma 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372. https:\/\/doi.org\/10.1136\/bmj.n71,https:\/\/www.bmj.com\/content\/372\/bmj.n71, https:\/\/arxiv.org\/abs\/https:\/\/www.bmj.com\/content\/372\/bmj.n71.full.pdf","DOI":"10.1136\/bmj.n71,"},{"issue":"1","key":"10563_CR175","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1109\/TNSE.2021.3050781","volume":"9","author":"Z Peng","year":"2021","unstructured":"Peng Z, Xu J, Chu X et al (2021) Vfchain: enabling verifiable and auditable federated learning via blockchain systems. IEEE Trans Netw Sci Eng 9(1):173\u2013186","journal-title":"IEEE Transactions on Network Science and Engineering"},{"key":"10563_CR176","unstructured":"Peterson D, Kanani P, Marathe VJ (2019) Private federated learning with domain adaptation. arXiv:1912.06733"},{"key":"10563_CR177","doi-asserted-by":"crossref","unstructured":"Prathiba SB, Raja G, Anbalagan S, et\u00a0al (2021) Federated learning empowered computation offloading and resource management in 6G-V2X. IEEE Trans Netw Sci Eng 9:3234\u20133243","DOI":"10.1109\/TNSE.2021.3103124"},{"key":"10563_CR179","doi-asserted-by":"crossref","first-page":"205,071","DOI":"10.1109\/ACCESS.2020.3037474","volume":"8","author":"MA Rahman","year":"2020","unstructured":"Rahman MA, Hossain MS, Islam MS et al (2020) Secure and provenance enhanced internet of health things framework: a blockchain managed federated learning approach. IEEE Access 8:205,071-205,087","journal-title":"Ieee Access"},{"key":"10563_CR178","doi-asserted-by":"crossref","first-page":"124,682","DOI":"10.1109\/ACCESS.2021.3111118","volume":"9","author":"KJ Rahman","year":"2021","unstructured":"Rahman KJ, Ahmed F, Akhter N et al (2021) Challenges, applications and design aspects of federated learning: a survey. IEEE Access 9:124,682-124,700","journal-title":"IEEE Access"},{"key":"10563_CR180","unstructured":"Reisizadeh A, Mokhtari A, Hassani H, et\u00a0al (2020) FedPAQ: a communication-efficient federated learning method with periodic averaging and quantization. In: International conference on artificial intelligence and statistics. PMLR, pp 2021\u20132031"},{"key":"10563_CR181","doi-asserted-by":"crossref","first-page":"69,194","DOI":"10.1109\/ACCESS.2019.2919736","volume":"7","author":"J Ren","year":"2019","unstructured":"Ren J, Wang H, Hou T et al (2019) Federated learning-based computation offloading optimization in edge computing-supported internet of things. IEEE Access 7:69,194-69,201","journal-title":"IEEE Access"},{"key":"10563_CR182","doi-asserted-by":"crossref","unstructured":"Ribero M, Vikalo H (2020) Communication-efficient federated learning via optimal client sampling. arXiv:2007.15197","DOI":"10.52591\/lxai2020071310"},{"key":"10563_CR183","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.inffus.2020.07.009","volume":"64","author":"N Rodr\u00edguez-Barroso","year":"2020","unstructured":"Rodr\u00edguez-Barroso N, Stipcich G, Jim\u00e9nez-L\u00f3pez D et al (2020) Federated learning and differential privacy: software tools analysis, the sherpa. ai fl framework and methodological guidelines for preserving data privacy. Inf Fusion 64:270\u2013292","journal-title":"Information Fusion"},{"key":"10563_CR184","unstructured":"Rodr\u00edguez-G\u00e1lvez B, Granqvist F, van Dalen R, et\u00a0al (2021) Enforcing fairness in private federated learning via the modified method of differential multipliers. arXiv:2109.08604"},{"key":"10563_CR185","unstructured":"Rothchild D, Panda A, Ullah E, et\u00a0al (2020) Fetchsgd: communication-efficient federated learning with sketching. In: International conference on machine learning. PMLR, pp 8253\u20138265"},{"key":"10563_CR186","unstructured":"Roy AG, Siddiqui S, P\u00f6lsterl S, et\u00a0al (2019) Braintorrent: a peer-to-peer environment for decentralized federated learning. arXiv:1905.06731"},{"key":"10563_CR187","unstructured":"Saputra YM, Nguyen DN, Hoang DT, et\u00a0al (2020) Federated learning meets contract theory: energy-efficient framework for electric vehicle networks. arXiv:2004.01828"},{"issue":"1","key":"10563_CR188","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1109\/LNET.2019.2947144","volume":"2","author":"Y Sarikaya","year":"2019","unstructured":"Sarikaya Y, Ercetin O (2019) Motivating workers in federated learning: a stackelberg game perspective. IEEE Netw Lett 2(1):23\u201327","journal-title":"IEEE Networking Letters"},{"issue":"9","key":"10563_CR189","doi-asserted-by":"crossref","first-page":"3400","DOI":"10.1109\/TNNLS.2019.2944481","volume":"31","author":"F Sattler","year":"2019","unstructured":"Sattler F, Wiedemann S, M\u00fcller KR et al (2019a) Robust and communication-efficient federated learning from non-iid data. IEEE Trans Neural Netw Learn Syst 31(9):3400\u20133413","journal-title":"IEEE transactions on neural networks and learning systems"},{"key":"10563_CR190","doi-asserted-by":"crossref","unstructured":"Sattler F, Wiedemann S, M\u00fcller KR, et\u00a0al (2019b) Sparse binary compression: towards distributed deep learning with minimal communication. In: 2019 international joint conference on neural networks (IJCNN). IEEE, pp 1\u20138","DOI":"10.1109\/IJCNN.2019.8852172"},{"key":"10563_CR191","doi-asserted-by":"crossref","unstructured":"Sattler F, M\u00fcller KR, Samek W (2020) Clustered federated learning: model-agnostic distributed multitask optimization under privacy constraints. IEEE Trans Neural Netw Learn Syst 32:3710\u20143722","DOI":"10.1109\/TNNLS.2020.3015958"},{"key":"10563_CR192","doi-asserted-by":"crossref","unstructured":"Seif M, Tandon R, Li M (2020) Wireless federated learning with local differential privacy. In: 2020 IEEE international symposium on information theory (ISIT). IEEE, pp 2604\u20132609","DOI":"10.1109\/ISIT44484.2020.9174426"},{"key":"10563_CR193","doi-asserted-by":"crossref","unstructured":"Shahid O, Pouriyeh S, Parizi RM, et\u00a0al (2021) Communication efficiency in federated learning: achievements and challenges. arXiv:2107.10996","DOI":"10.3390\/app12188980"},{"key":"10563_CR194","doi-asserted-by":"crossref","unstructured":"Shen S, Tople S, Saxena P (2016) Auror: Defending against poisoning attacks in collaborative deep learning systems. In: Proceedings of the 32nd annual conference on computer security applications, pp 508\u2013519","DOI":"10.1145\/2991079.2991125"},{"key":"10563_CR195","unstructured":"Shi Y, Yu H, Leung C (2021) A survey of fairness-aware federated learning. arXiv:2111.01872"},{"key":"10563_CR196","unstructured":"Shin M, Hwang C, Kim J, et\u00a0al (2020) Xor mixup: privacy-preserving data augmentation for one-shot federated learning. arXiv:2006.05148"},{"key":"10563_CR197","doi-asserted-by":"crossref","unstructured":"Shlezinger N, Chen M, Eldar YC et al (2020) Federated learning with quantization constraints. In: ICASSP 2020\u20132020 IEEE international conference on acoustics. Speech and Signal Processing (ICASSP). IEEE, pp 8851\u20138855","DOI":"10.1109\/ICASSP40776.2020.9054168"},{"key":"10563_CR198","unstructured":"Shyn SK, Kim D, Kim K (2021) Fedccea: A practical approach of client contribution evaluation for federated learning. arXiv:2106.02310"},{"key":"10563_CR199","unstructured":"Smith V, Chiang CK, Sanjabi M, et\u00a0al (2017) Federated multi-task learning. arXiv:1705.10467"},{"key":"10563_CR200","doi-asserted-by":"crossref","unstructured":"Song T, Tong Y, Wei S (2019) Profit allocation for federated learning. In: 2019 IEEE international conference on big data (Big Data). IEEE, pp 2577\u20132586","DOI":"10.1109\/BigData47090.2019.9006327"},{"issue":"2","key":"10563_CR201","doi-asserted-by":"crossref","first-page":"1212","DOI":"10.1109\/JIOT.2021.3079104","volume":"9","author":"Z Song","year":"2021","unstructured":"Song Z, Sun H, Yang HH et al (2021) Reputation-based federated learning for secure wireless networks. IEEE Internet Things J 9(2):1212\u20131226","journal-title":"IEEE Internet Things J"},{"key":"10563_CR202","doi-asserted-by":"crossref","unstructured":"Strom N (2015) Scalable distributed dnn training using commodity gpu cloud computing. In: Sixteenth annual conference of the international speech communication association","DOI":"10.21437\/Interspeech.2015-354"},{"key":"10563_CR203","doi-asserted-by":"crossref","unstructured":"Su L, Liu Z, Ye J (2022) Reputation-based defense scheme against backdoor attacks on federated learning. In: 2021 international conference on big data analytics for cyber-physical system in smart city. Springer, New York, pp 949\u2013955","DOI":"10.1007\/978-981-16-7469-3_107"},{"key":"10563_CR204","volume-title":"Blockchain: Blueprint for a new economy","author":"M Swan","year":"2015","unstructured":"Swan M (2015) Blockchain: blueprint for a new economy. O\u2019Reilly Media Inc, Sebastopol"},{"key":"10563_CR205","doi-asserted-by":"crossref","unstructured":"Tan AZ, Yu H, Cui L, et\u00a0al (2021) Towards personalized federated learning. arXiv:2103.00710","DOI":"10.1109\/TNNLS.2022.3160699"},{"key":"10563_CR206","doi-asserted-by":"crossref","unstructured":"Torrey L, Shavlik J (2010) Transfer learning. In: Handbook of research on machine learning applications and trends: algorithms, methods, and techniques. IGI global, p 242\u2013264","DOI":"10.4018\/978-1-60566-766-9.ch011"},{"key":"10563_CR207","doi-asserted-by":"crossref","unstructured":"Toyoda K, Zhang AN (2019) Mechanism design for an incentive-aware blockchain-enabled federated learning platform. In: 2019 IEEE international conference on big sata (Big Data). IEEE, pp 395\u2013403","DOI":"10.1109\/BigData47090.2019.9006344"},{"key":"10563_CR208","doi-asserted-by":"publisher","unstructured":"Triastcyn A, Faltings B (2019) Federated learning with bayesian differential privacy. In: 2019 IEEE international conference on big data (Big Data). https:\/\/doi.org\/10.1109\/bigdata47090.2019.9005465","DOI":"10.1109\/bigdata47090.2019.9005465"},{"issue":"4","key":"10563_CR209","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MIS.2020.2993966","volume":"35","author":"A Triastcyn","year":"2020","unstructured":"Triastcyn A, Faltings B (2020) Federated generative privacy. IEEE Intell Syst 35(4):50\u201357","journal-title":"IEEE Intell Syst"},{"key":"10563_CR210","doi-asserted-by":"crossref","unstructured":"Truex S, Baracaldo N, Anwar A, et\u00a0al (2019) A hybrid approach to privacy-preserving federated learning. In: Proceedings of the 12th ACM workshop on artificial intelligence and security, pp 1\u201311","DOI":"10.1145\/3338501.3357370"},{"key":"10563_CR211","doi-asserted-by":"crossref","unstructured":"Truex S, Liu L, Chow KH, et\u00a0al (2020) LDP-Fed: federated learning with local differential privacy. In: Proceedings of the third ACM international workshop on edge systems, analytics and networking, pp 61\u201366","DOI":"10.1145\/3378679.3394533"},{"key":"10563_CR212","doi-asserted-by":"publisher","unstructured":"Tu X, Zhu K, Luong NC, et\u00a0al (2022) Incentive mechanisms for federated learning: from economic and game theoretic perspective. IEEE Trans Cognit Commun Netw pp 1\u20131. https:\/\/doi.org\/10.1109\/TCCN.2022.3177522","DOI":"10.1109\/TCCN.2022.3177522"},{"key":"10563_CR213","unstructured":"Tyagi N (2022) What is differential privacy and how does it work? Analytics steps. https:\/\/www.analyticssteps.com\/blogs\/what-differential-privacy-and-how-does-it-work"},{"key":"10563_CR214","doi-asserted-by":"crossref","unstructured":"Vahidian S, Morafah M, Lin B (2021) Personalized federated learning by structured and unstructured pruning under data heterogeneity. arXiv:2105.00562","DOI":"10.1109\/ICDCSW53096.2021.00012"},{"issue":"1","key":"10563_CR215","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1198\/10618600152418584","volume":"10","author":"DA Van Dyk","year":"2001","unstructured":"Van Dyk DA, Meng XL (2001) The art of data augmentation. J Comput Graph Stat 10(1):1\u201350","journal-title":"J Comput Graph Stat"},{"key":"10563_CR216","doi-asserted-by":"crossref","unstructured":"Vy NC, Quyen NH, Pham VH, et\u00a0al (2021) Federated learning-based intrusion detection in the context of iiot networks: poisoning attack and defense. In: International conference on network and system security. Springer, New York, pp 131\u2013147","DOI":"10.1007\/978-3-030-92708-0_8"},{"key":"10563_CR217","doi-asserted-by":"crossref","unstructured":"Wainakh A, Guinea AS, Grube T, et\u00a0al (2020) Enhancing privacy via hierarchical federated learning. In: 2020 IEEE European symposium on security and privacy workshops (EuroS &PW). IEEE, pp 344\u2013347","DOI":"10.1109\/EuroSPW51379.2020.00053"},{"key":"10563_CR218","doi-asserted-by":"crossref","unstructured":"Wan W, Lu J, Hu S, et\u00a0al (2021) Shielding federated learning: a new attack approach and its defense. In: 2021 IEEE wireless communications and networking conference (WCNC). IEEE, pp 1\u20137","DOI":"10.1109\/WCNC49053.2021.9417334"},{"key":"10563_CR221","doi-asserted-by":"crossref","unstructured":"Wang J, Chen Y, Hao S, et\u00a0al (2017) Balanced distribution adaptation for transfer learning. In: 2017 IEEE international conference on data mining (ICDM). IEEE, pp 1129\u20131134","DOI":"10.1109\/ICDM.2017.150"},{"key":"10563_CR220","doi-asserted-by":"publisher","unstructured":"Wang G, Dang CX, Zhou Z (2019a) Measure contribution of participants in federated learning. In: 2019 IEEE international conference on big data (Big Data), pp 2597\u20132604, https:\/\/doi.org\/10.1109\/BigData47090.2019.9006179","DOI":"10.1109\/BigData47090.2019.9006179"},{"key":"10563_CR223","doi-asserted-by":"crossref","unstructured":"Wang L, Wang W, Li B (2019b) CMFL: mitigating communication overhead for federated learning. In: 2019 IEEE 39th international conference on distributed computing systems (ICDCS), pp 954\u2013964","DOI":"10.1109\/ICDCS.2019.00099"},{"issue":"5","key":"10563_CR226","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/MNET.2019.1800286","volume":"33","author":"X Wang","year":"2019","unstructured":"Wang X, Han Y, Wang C et al (2019c) In-edge ai: intelligentizing mobile edge computing, caching and communication by federated learning. IEEE Network 33(5):156\u2013165","journal-title":"IEEE Network"},{"key":"10563_CR225","doi-asserted-by":"crossref","unstructured":"Wang T, Rausch J, Zhang C, et\u00a0al (2020a) A principled approach to data valuation for federated learning. In: Federated learning. Springer, p 153\u2013167","DOI":"10.1007\/978-3-030-63076-8_11"},{"key":"10563_CR227","doi-asserted-by":"crossref","unstructured":"Wang Y, Zhu T, Chang W, et\u00a0al (2020b) Model poisoning defense on federated learning: a validation based approach. In: International conference on network and system security. Springer, New York, pp 207\u2013223","DOI":"10.1007\/978-3-030-65745-1_12"},{"key":"10563_CR228","unstructured":"Wang Z, Yang Y, Liu Y, et\u00a0al (2020c) Cloud-based federated boosting for mobile crowdsensing. arXiv:2005.05304"},{"issue":"4","key":"10563_CR219","doi-asserted-by":"crossref","first-page":"308","DOI":"10.23919\/CSMS.2021.0026","volume":"1","author":"C Wang","year":"2021","unstructured":"Wang C, Liu Z, Wei H et al (2021a) Hybrid deep learning model for short-term wind speed forecasting based on time series decomposition and gated recurrent unit. Complex Syst Model Simul 1(4):308\u2013321","journal-title":"Complex System Modeling and Simulation"},{"key":"10563_CR222","unstructured":"Wang J, Charles Z, Xu Z, et\u00a0al (2021b) A field guide to federated optimization. arXiv:2107.06917"},{"issue":"24","key":"10563_CR224","doi-asserted-by":"crossref","first-page":"17,460","DOI":"10.1109\/JIOT.2021.3080078","volume":"8","author":"S Wang","year":"2021","unstructured":"Wang S, Chen M, Yin C et al (2021c) Federated learning for task and resource allocation in wireless high-altitude balloon networks. IEEE Internet Things J 8(24):17,460-17,475","journal-title":"IEEE Internet Things J"},{"key":"10563_CR229","doi-asserted-by":"crossref","unstructured":"Wang Z, Fan X, Qi J, et\u00a0al (2021d) Federated learning with fair averaging. arXiv:2104.14937","DOI":"10.24963\/ijcai.2021\/223"},{"key":"10563_CR233","doi-asserted-by":"crossref","unstructured":"Wei X, Li Q, Liu Y, et\u00a0al (2019) Multi-agent visualization for explaining federated learning. In: IJCAI, pp 6572\u20136574","DOI":"10.24963\/ijcai.2019\/960"},{"key":"10563_CR230","doi-asserted-by":"crossref","first-page":"3454","DOI":"10.1109\/TIFS.2020.2988575","volume":"15","author":"K Wei","year":"2020","unstructured":"Wei K, Li J, Ding M et al (2020a) Federated learning with differential privacy: algorithms and performance analysis. IEEE Trans Inf Forensics Secur 15:3454\u20133469","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"10563_CR232","doi-asserted-by":"crossref","unstructured":"Wei W, Liu L, Loper M, et\u00a0al (2020b) A framework for evaluating client privacy leakages in federated learning. In: European symposium on research in computer security. Springer, New York, pp 545\u2013566","DOI":"10.1007\/978-3-030-58951-6_27"},{"key":"10563_CR231","doi-asserted-by":"crossref","unstructured":"Wei K, Li J, Ding M, et\u00a0al (2021) User-level privacy-preserving federated learning: analysis and performance optimization. IEEE Trans Mob Comput","DOI":"10.1109\/TMC.2021.3056991"},{"key":"10563_CR234","unstructured":"Wen W, Xu C, Yan F, et\u00a0al (2017) Terngrad: Ternary gradients to reduce communication in distributed deep learning. In: Advances in neural information processing systems, p 30"},{"key":"10563_CR235","doi-asserted-by":"crossref","unstructured":"Weng J, Weng J, Zhang J, et\u00a0al (2019) Deepchain: auditable and privacy-preserving deep learning with blockchain-based incentive. IEEE Trans Depend Secure Comput","DOI":"10.1109\/TDSC.2019.2952332"},{"key":"10563_CR236","doi-asserted-by":"crossref","unstructured":"Weng J, Weng J, Huang H, et\u00a0al (2021) Fedserving: a federated prediction serving framework based on incentive mechanism. In: IEEE INFOCOM 2021-IEEE conference on computer communications. IEEE, pp 1\u201310","DOI":"10.1109\/INFOCOM42981.2021.9488807"},{"key":"10563_CR237","doi-asserted-by":"publisher","unstructured":"Wohlin C (2014) Guidelines for snowballing in systematic literature studies and a replication in software engineering. In: Proceedings of the 18th international conference on evaluation and assessment in software engineering. Association for Computing Machinery, EASE \u201914, https:\/\/doi.org\/10.1145\/2601248.2601268","DOI":"10.1145\/2601248.2601268"},{"key":"10563_CR240","unstructured":"Wu Q, Chen X, Zhou Z, et\u00a0al (2020a) Fedhome: Cloud-edge based personalized federated learning for in-home health monitoring. IEEE Trans Mobile Comput"},{"key":"10563_CR241","doi-asserted-by":"crossref","unstructured":"Wu Y, Cai S, Xiao X, et\u00a0al (2020b) Privacy preserving vertical federated learning for tree-based models. arXiv:2008.06170","DOI":"10.14778\/3407790.3407811"},{"key":"10563_CR238","doi-asserted-by":"crossref","unstructured":"Wu C, Wu F, Liu R, et\u00a0al (2021a) Fedkd: Communication efficient federated learning via knowledge distillation. arXiv:2108.13323","DOI":"10.1038\/s41467-022-29763-x"},{"key":"10563_CR239","first-page":"957","volume":"2021","author":"J Wu","year":"2021","unstructured":"Wu J, Liu Q, Huang Z et al (2021b) Hierarchical personalized federated learning for user modeling. Proc Web Conf 2021:957\u2013968","journal-title":"Proceedings of the Web Conference"},{"key":"10563_CR242","unstructured":"Xie M, Long G, Shen T, et\u00a0al (2021) Multi-center federated learning. arXiv:2108.08647"},{"key":"10563_CR245","unstructured":"Xu X, Lyu L (2020) Towards building a robust and fair federated learning system. arXiv e-prints pp arXiv\u20132011"},{"key":"10563_CR244","doi-asserted-by":"crossref","unstructured":"Xu R, Baracaldo N, Zhou Y, et\u00a0al (2019a) Hybridalpha: An efficient approach for privacy-preserving federated learning. In: Proceedings of the 12th ACM workshop on artificial intelligence and security. pp 13\u201323","DOI":"10.1145\/3338501.3357371"},{"issue":"r1","key":"10563_CR246","volume":"2","author":"Z Xu","year":"2019","unstructured":"Xu Z, Yang Z, Xiong J et al (2019b) Elfish: Resource-aware federated learning on heterogeneous edge devices. Ratio 2(r1):r2","journal-title":"Ratio"},{"issue":"1","key":"10563_CR243","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s41666-020-00082-4","volume":"5","author":"J Xu","year":"2021","unstructured":"Xu J, Glicksberg BS, Su C et al (2021) Federated learning for healthcare informatics. J Healthc Inf Res 5(1):1\u201319","journal-title":"Journal of Healthcare Informatics Research"},{"key":"10563_CR250","doi-asserted-by":"publisher","unstructured":"Yang Q, Liu Y, Chen T, et\u00a0al (2019) Federated machine learning: concept and applications. ACM Trans Intell Syst Technol 10(2). https:\/\/doi.org\/10.1145\/3298981","DOI":"10.1145\/3298981"},{"key":"10563_CR247","unstructured":"Yang G, Mu K, Song C, et\u00a0al (2021a) Ringfed: Reducing communication costs in federated learning on non-iid data. arXiv:2107.08873"},{"key":"10563_CR248","doi-asserted-by":"crossref","unstructured":"Yang G, Wang S, Wang H (2021b) Federated learning with personalized local differential privacy. In: 2021 IEEE 6th international conference on computer and communication systems (ICCCS). IEEE, pp 484\u2013489","DOI":"10.1109\/ICCCS52626.2021.9449232"},{"key":"10563_CR249","doi-asserted-by":"crossref","unstructured":"Yang M, Wang X, Zhu H, et\u00a0al (2021c) Federated learning with class imbalance reduction. In: 2021 29th European signal processing conference (EUSIPCO). IEEE, pp 2174\u20132178","DOI":"10.23919\/EUSIPCO54536.2021.9616052"},{"key":"10563_CR251","doi-asserted-by":"crossref","unstructured":"Yao X, Huang C, Sun L (2018) Two-stream federated learning: reduce the communication costs. In: 2018 IEEE visual communications and image processing (VCIP). IEEE, pp 1\u20134","DOI":"10.1109\/VCIP.2018.8698609"},{"key":"10563_CR252","doi-asserted-by":"crossref","unstructured":"Yao X, Huang T, Wu C, et\u00a0al (2019a) Towards faster and better federated learning: a feature fusion approach. In: 2019 IEEE international conference on image processing (ICIP). IEEE, pp 175\u2013179","DOI":"10.1109\/ICIP.2019.8803001"},{"key":"10563_CR253","unstructured":"Yao X, Huang T, Wu C, et\u00a0al (2019b) Federated learning with additional mechanisms on clients to reduce communication costs. arXiv:1908.05891"},{"key":"10563_CR254","doi-asserted-by":"crossref","first-page":"23,920","DOI":"10.1109\/ACCESS.2020.2968399","volume":"8","author":"D Ye","year":"2020","unstructured":"Ye D, Yu R, Pan M et al (2020) Federated learning in vehicular edge computing: a selective model aggregation approach. IEEE Access 8:23,920-23,935","journal-title":"IEEE Access"},{"key":"10563_CR255","doi-asserted-by":"crossref","unstructured":"Yi\u00a0Ming W, Ge\u00a0Hao L, Li\u00a0Yu F, et\u00a0al (2021) Research on block chain defense against malicious attack in federated learning. In: 2021 the 3rd international conference on blockchain technology, pp 67\u201372","DOI":"10.1145\/3460537.3460540"},{"key":"10563_CR256","doi-asserted-by":"crossref","unstructured":"Yoo JH, Son HM, Jeong H, et\u00a0al (2021) Personalized federated learning with clustering: non-iid heart rate variability data application. In: 2021 International conference on information and communication technology convergence (ICTC). IEEE, pp 1046\u20131051","DOI":"10.1109\/ICTC52510.2021.9620852"},{"key":"10563_CR257","doi-asserted-by":"crossref","unstructured":"Yu H, Liu Z, Liu Y, et\u00a0al (2020a) A fairness-aware incentive scheme for federated learning. In: Proceedings of the AAAI\/ACM conference on AI, ethics, and society. pp 393\u2013399","DOI":"10.1145\/3375627.3375840"},{"key":"10563_CR258","unstructured":"Yu P, Kundu A, Wynter L, et\u00a0al (2020b) Fed+: a unified approach to robust personalized federated learning. arXiv:2009.06303"},{"key":"10563_CR259","doi-asserted-by":"crossref","unstructured":"Yu S, Chen X, Zhou Z, et\u00a0al (2020c) When deep reinforcement learning meets federated learning: intelligent multi-timescale resource management for multi-access edge computing in 5G ultra dense network. arXiv:2009.10601","DOI":"10.1109\/JIOT.2020.3026589"},{"key":"10563_CR260","unstructured":"Yu T, Bagdasaryan E, Shmatikov V (2020d) Salvaging federated learning by local adaptation. arXiv:2002.04758"},{"key":"10563_CR261","doi-asserted-by":"crossref","unstructured":"Yu T, Li T, Sun Y, et\u00a0al (2020e) Learning context-aware policies from multiple smart homes via federated multi-task learning. In: 2020 IEEE\/ACM fifth international conference on internet-of-things design and implementation (IoTDI). IEEE, pp 104\u2013115","DOI":"10.1109\/IoTDI49375.2020.00017"},{"key":"10563_CR262","doi-asserted-by":"crossref","unstructured":"Yuan X, Ma X, Zhang L, et\u00a0al (2021) Beyond class-level privacy leakage: breaking record-level privacy in federated learning. IEEE Internet Things J","DOI":"10.1109\/JIOT.2021.3089713"},{"key":"10563_CR264","unstructured":"Yue X, Kontar RA (2021) Federated gaussian process: convergence, automatic personalization and multi-fidelity modeling. arXiv:2111.14008"},{"key":"10563_CR265","unstructured":"Yue X, Nouiehed M, Kontar RA (2021) Gifair-fl: An approach for group and individual fairness in federated learning. arXiv:2108.02741"},{"key":"10563_CR263","doi-asserted-by":"crossref","unstructured":"Yue K, Jin R, Wong CW, et\u00a0al (2022) Communication-efficient federated learning via predictive coding. IEEE J Select Top Signal Process 16:369\u2013380","DOI":"10.1109\/JSTSP.2022.3142678"},{"key":"10563_CR266","unstructured":"Yurochkin M, Agarwal M, Ghosh S, et\u00a0al (2019) Bayesian nonparametric federated learning of neural networks. In: International conference on machine learning. PMLR, pp 7252\u20137261"},{"key":"10563_CR267","doi-asserted-by":"crossref","unstructured":"Zeng R, Zhang S, Wang J, et\u00a0al (2020) Fmore: An incentive scheme of multi-dimensional auction for federated learning in mec. In: 2020 IEEE 40th international conference on distributed computing systems (ICDCS). IEEE, pp 278\u2013288","DOI":"10.1109\/ICDCS47774.2020.00094"},{"key":"10563_CR268","unstructured":"Zeng R, Zeng C, Wang X, et\u00a0al (2021) A comprehensive survey of incentive mechanism for federated learning. arXiv:2106.15406"},{"issue":"7","key":"10563_CR269","doi-asserted-by":"crossref","first-page":"6360","DOI":"10.1109\/JIOT.2020.2967772","volume":"7","author":"Y Zhan","year":"2020","unstructured":"Zhan Y, Li P, Qu Z et al (2020) A learning-based incentive mechanism for federated learning. IEEE Internet Things J 7(7):6360\u20136368","journal-title":"IEEE Internet Things J"},{"key":"10563_CR270","doi-asserted-by":"crossref","unstructured":"Zhan Y, Zhang J, Hong Z, et\u00a0al (2021) A survey of incentive mechanism design for federated learning. IEEE Trans Emerg Top Comput 10:1035\u20131044","DOI":"10.1109\/TETC.2021.3063517"},{"key":"10563_CR280","unstructured":"Zhang X, Luo X (2020) Exploiting defenses against gan-based feature inference attacks in federated learning. arXiv:2004.12571"},{"key":"10563_CR275","doi-asserted-by":"crossref","unstructured":"Zhang J, Chen J, Wu D, et\u00a0al (2019) Poisoning attack in federated learning using generative adversarial nets. In: 2019 18th IEEE international conference on trust, security and privacy in computing and communications\/13th IEEE international conference on big data science and engineering (TrustCom\/BigDataSE). IEEE, pp 374\u2013380","DOI":"10.1109\/TrustCom\/BigDataSE.2019.00057"},{"key":"10563_CR272","doi-asserted-by":"crossref","unstructured":"Zhang DY, Kou Z, Wang D (2020a) FairFL: a fair federated learning approach to reducing demographic bias in privacy-sensitive classification models. In: 2020 IEEE international conference on big data (Big Data). IEEE, pp 1051\u20131060","DOI":"10.1109\/BigData50022.2020.9378043"},{"key":"10563_CR276","unstructured":"Zhang J, Li C, Robles-Kelly A, et\u00a0al (2020b) Hierarchically fair federated learning. arXiv:2004.10386"},{"key":"10563_CR279","unstructured":"Zhang M, Sapra K, Fidler S, et\u00a0al (2020c) Personalized federated learning with first order model optimization. arXiv:2012.08565"},{"issue":"106","key":"10563_CR271","first-page":"775","volume":"216","author":"C Zhang","year":"2021","unstructured":"Zhang C, Xie Y, Bai H et al (2021a) A survey on federated learning. Knowl-Based Syst 216(106):775","journal-title":"Knowl-Based Syst"},{"key":"10563_CR273","doi-asserted-by":"crossref","unstructured":"Zhang DY, Kou Z, Wang D (2021b) Fedsens: a federated learning approach for smart health sensing with class imbalance in resource constrained edge computing. In: IEEE INFOCOM 2021-IEEE conference on computer communications. IEEE, pp 1\u201310","DOI":"10.1109\/INFOCOM42981.2021.9488776"},{"key":"10563_CR274","unstructured":"Zhang F, Kuang K, Liu Y, et\u00a0al (2021c) Unified group fairness on federated learning. arXiv:2111.04986"},{"key":"10563_CR277","unstructured":"Zhang J, Guo S, Ma X, et\u00a0al (2021d) Parameterized knowledge transfer for personalized federated learning. Adv Neural Inf Process Syst 34:10092\u201310104"},{"key":"10563_CR278","first-page":"947","volume":"2021","author":"J Zhang","year":"2021","unstructured":"Zhang J, Wu Y, Pan R (2021e) Incentive mechanism for horizontal federated learning based on reputation and reverse auction. Proc Web Conf 2021:947\u2013956","journal-title":"Proceedings of the Web Conference"},{"issue":"12","key":"10563_CR281","doi-asserted-by":"crossref","first-page":"2659","DOI":"10.14778\/3476311.3476313","volume":"14","author":"Z Zhang","year":"2021","unstructured":"Zhang Z, Dong D, Ma Y et al (2021f) Refiner: a reliable incentive-driven federated learning system powered by blockchain. Proc VLDB Endow 14(12):2659\u20132662","journal-title":"Proceedings of the VLDB Endowment"},{"key":"10563_CR283","doi-asserted-by":"crossref","unstructured":"Zhao Y, Chen J, Zhang J, et\u00a0al (2019) PDGAN: a novel poisoning defense method in federated learning using generative adversarial network. In: International conference on algorithms and architectures for parallel processing. Springer, New York, pp 595\u2013609","DOI":"10.1007\/978-3-030-38991-8_39"},{"issue":"3","key":"10563_CR284","doi-asserted-by":"crossref","first-page":"1817","DOI":"10.1109\/JIOT.2020.3017377","volume":"8","author":"Y Zhao","year":"2020","unstructured":"Zhao Y, Zhao J, Jiang L et al (2020a) Privacy-preserving blockchain-based federated learning for iot devices. IEEE Internet Things J 8(3):1817\u20131829","journal-title":"IEEE Internet Things J"},{"issue":"11","key":"10563_CR285","doi-asserted-by":"crossref","first-page":"8836","DOI":"10.1109\/JIOT.2020.3037194","volume":"8","author":"Y Zhao","year":"2020","unstructured":"Zhao Y, Zhao J, Yang M et al (2020b) Local differential privacy-based federated learning for internet of things. IEEE Internet Things J 8(11):8836\u20138853","journal-title":"IEEE Internet Things J"},{"key":"10563_CR282","doi-asserted-by":"crossref","unstructured":"Zhao C, Wen Y, Li S, et\u00a0al (2021) Federatedreverse: a detection and defense method against backdoor attacks in federated learning. In: Proceedings of the 2021 ACM workshop on information hiding and multimedia security, pp 51\u201362","DOI":"10.1145\/3437880.3460403"},{"key":"10563_CR286","doi-asserted-by":"crossref","unstructured":"Zhou H, Cheng J, Wang X, et\u00a0al (2020) Low rank communication for federated learning. In: International conference on database systems for advanced applications. Springer, New York, pp 1\u201316","DOI":"10.1007\/978-3-030-59413-8_1"},{"issue":"1","key":"10563_CR287","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1109\/TPDS.2021.3090331","volume":"33","author":"Y Zhou","year":"2021","unstructured":"Zhou Y, Ye Q, Lv J (2021) Communication-efficient federated learning with compensated overlap-fedavg. IEEE Trans Parallel Distrib Syst 33(1):192\u2013205","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"10563_CR288","doi-asserted-by":"crossref","unstructured":"Zhu L, Han S (2020) Deep leakage from gradients. In: Federated learning. Springer, New York, pp 17\u201331","DOI":"10.1007\/978-3-030-63076-8_2"},{"key":"10563_CR289","doi-asserted-by":"crossref","unstructured":"Zhuang W, Wen Y, Zhang X, et\u00a0al (2020) Performance optimization of federated person re-identification via benchmark analysis. In: Proceedings of the 28th ACM international conference on multimedia, pp 955\u2013963","DOI":"10.1145\/3394171.3413814"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-023-10563-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-023-10563-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-023-10563-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T19:12:21Z","timestamp":1699902741000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-023-10563-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,7]]},"references-count":289,"journal-issue":{"issue":"S2","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["10563"],"URL":"https:\/\/doi.org\/10.1007\/s10462-023-10563-8","relation":{},"ISSN":["0269-2821","1573-7462"],"issn-type":[{"value":"0269-2821","type":"print"},{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,7]]},"assertion":[{"value":"7 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"YS: Conceptualisation, investigation, methodology, writing\u2014original draft. HP: Conceptualisation, methodology, data curation, writing\u2014original draft. SSK: Conceptualisation, supervision, writing\u2014review and editing LZ: Supervision, writing\u2014review and editing.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Author contributions"}}]}}