{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T02:56:58Z","timestamp":1792897018895,"version":"4.5.0"},"reference-count":134,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T00:00:00Z","timestamp":1714348800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T00:00:00Z","timestamp":1714348800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61862007"],"award-info":[{"award-number":["61862007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012547","name":"Natural Science Foundation of Guangxi Zhuang Autonomous Region","doi-asserted-by":"publisher","award":["2020GXNSFBA297103"],"award-info":[{"award-number":["2020GXNSFBA297103"]}],"id":[{"id":"10.13039\/100012547","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The amount of data generated owing to the rapid development of the Smart Internet of Things is increasing exponentially. Traditional machine learning can no longer meet the requirements for training complex models with large amounts of data. Federated learning, as a new paradigm for training statistical models in distributed edge networks, alleviates integration and training problems in the context of massive and heterogeneous data and security protection for private data. Edge computing processes data at the edge layers of data sources to ensure low-data-delay processing; it provides high-bandwidth communication and a stable network environment, and relieves the pressure of processing massive data using a single node in the cloud center. A combination of edge computing and federated learning can further optimize computing, communication, and data security for the edge-Internet of Things. This review investigated the development status of federated learning and expounded on its basic principles. Then, in view of the security attacks and privacy leakage problems of federated learning in the edge Internet of things, relevant work was investigated from cryptographic technologies (such as secure multi-party computation, homomorphic encryption and secret sharing), perturbation schemes (such as differential privacy), adversarial training and other privacy security protection measures. Finally, challenges and future research directions for the integration of edge computing and federated learning are discussed.<\/jats:p>","DOI":"10.1007\/s10462-024-10774-7","type":"journal-article","created":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T04:02:20Z","timestamp":1714363340000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":118,"title":["Survey: federated learning data security and privacy-preserving in edge-Internet of Things"],"prefix":"10.1007","volume":"57","author":[{"given":"Haiao","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lina","family":"Ge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,29]]},"reference":[{"issue":"5","key":"10774_CR1","doi-asserted-by":"publisher","first-page":"2740","DOI":"10.1109\/TNSE.2023.3293695","volume":"10","author":"M Adhikari","year":"2023","unstructured":"Adhikari M, Menon VG, Rawat DB, Li XW (2023) Guest Editorial Introduction to the Special Section on Computational Intelligence and Advanced Learning for Next-Generation Industrial IoT. IEEE Transac Network Sci Eng 10(5):2740\u20132744","journal-title":"IEEE Transac Network Sci Eng"},{"key":"10774_CR2","doi-asserted-by":"publisher","first-page":"100568","DOI":"10.1016\/j.cosrev.2023.100568","volume":"49","author":"S Ahmad","year":"2023","unstructured":"Ahmad S, Shakeel I, Mehfuz S, Ahmad J (2023) Deep learning models for cloud, edge, fog, and IoT computing paradigms: Survey, recent advances, and future directions. Computer Sci Rev 49:100568","journal-title":"Computer Sci Rev"},{"key":"10774_CR3","first-page":"17455","volume":"34","author":"G Andrew","year":"2021","unstructured":"Andrew G, Thakkar O, McMahan B, Ramaswamy S (2021) Differentially private learning with adaptive clipping. Adv Neural Inf Process Syst 34:17455\u201317466","journal-title":"Adv Neural Inf Process Syst"},{"key":"10774_CR4","unstructured":"Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks. In: Proceedings of the 34th International Conference on Machine Learning, vol 70, pp. 214\u2013223. PMLR, Sydney, NSW, Australia"},{"key":"10774_CR5","doi-asserted-by":"crossref","unstructured":"Baracaldo N, Chen B, Ludwig H, Safavi JA (2017) Mitigating poisoning attacks on machine learning models: A data provenance-based approach. In: Proceedings of the 10th ACM Workshop on Artificial Intelligence and Security, pp. 103\u2013110. ACM, Dallas, Texas, USA","DOI":"10.1145\/3128572.3140450"},{"key":"10774_CR6","doi-asserted-by":"publisher","first-page":"102679","DOI":"10.1016\/j.cose.2022.102679","volume":"117","author":"C Berry","year":"2022","unstructured":"Berry C, Komninos N (2022) Efficient optimisation framework for convolutional neural networks with secure multiparty computation. Comput Secur 117:102679","journal-title":"Comput Secur"},{"key":"10774_CR7","unstructured":"Bhagoji AN, Chakraborty S, Mittal P, Calo S (2019) Analyzing federated learning through an adversarial lens. In: Proceedings of the 36th International Conference on Machine Learning, pp. 634\u2013643. PMLR, Long Beach, California, USA"},{"key":"10774_CR8","first-page":"192","volume-title":"European Symposium on Research in Computer Security","author":"D Bogdanov","year":"2008","unstructured":"Bogdanov D, Laur S, Willemson J (2008) Sharemind: A framework for fast privacy-preserving computations. European Symposium on Research in Computer Security. Springer, Berlin, Heidelberg, pp 192\u2013206"},{"key":"10774_CR9","unstructured":"Bu ZQ, Wang H, Dai ZY, Long Q (2021) On the convergence and calibration of deep learning with differential privacy. arXiv preprint arXiv:2106.07830"},{"key":"10774_CR10","doi-asserted-by":"publisher","first-page":"122410","DOI":"10.1016\/j.eswa.2023.122410","volume":"239","author":"YH Cao","year":"2024","unstructured":"Cao YH, Zhang JB, Zhao YR, Su PC, Huang HX (2024) SRFL: A Secure & Robust Federated Learning framework for IoT with trusted execution environments. Expert Syst Appl 239:122410","journal-title":"Expert Syst Appl"},{"key":"10774_CR11","doi-asserted-by":"publisher","first-page":"5749","DOI":"10.1109\/TIFS.2023.3315125","volume":"18","author":"X Chen","year":"2023","unstructured":"Chen X, Yu HN, Jia XH, Yu XZ (2023) APFed: Anti-Poisoning Attacks in Privacy-Preserving Heterogeneous Federated Learning. IEEE Trans Inf Forensics Secur 18:5749\u20135761","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"10774_CR12","unstructured":"Croce F, Hein M (2020) Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. In: International conference on machine learning, pp.2206\u20132216, PMLR, Virtual Event"},{"key":"10774_CR13","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1007\/3-540-45748-8_24","volume-title":"International workshop on peer-to-peer systems","author":"JR Douceur","year":"2002","unstructured":"Douceur JR (2002) The sybil attack. International workshop on peer-to-peer systems, vol 2429. Springer, Berlin, Heidelberg, pp 251\u2013260"},{"issue":"6","key":"10774_CR14","doi-asserted-by":"publisher","first-page":"3193","DOI":"10.1109\/TMC.2021.3140080","volume":"22","author":"Y Du","year":"2023","unstructured":"Du Y, Wang Z, Li J, Shi L, Jayakody DNK, Chen W, Han Z (2023b) Blockchain-Aided Edge Computing Market: Smart Contract and Consensus Mechanisms[J]. IEEE Trans Mob Comput 22(6):3193\u20133280","journal-title":"IEEE Trans Mob Comput"},{"key":"10774_CR15","doi-asserted-by":"crossref","unstructured":"Du J, Qin N, Huang D, Jia XM, Zhang YM (2023) An Efficient Federated Learning Framework for Machinery Fault Diagnosis with Improved Model Aggregation and Local Model Training. IEEE Transactions on Neural Networks and Learning Systems 1\u201324. (Early Access)","DOI":"10.1109\/TNNLS.2023.3238724"},{"key":"10774_CR16","doi-asserted-by":"publisher","first-page":"892499","DOI":"10.3389\/fpubh.2022.892499","volume":"10","author":"R Durga","year":"2022","unstructured":"Durga R, Poovammal E (2022) FLED-Block: Federated Learning Ensembled Deep Learning Blockchain Model for COVID-19 Prediction. Front Public Health 10:892499","journal-title":"Front Public Health"},{"key":"10774_CR17","doi-asserted-by":"publisher","first-page":"106648","DOI":"10.1016\/j.compag.2021.106648","volume":"193","author":"A Durrant","year":"2022","unstructured":"Durrant A, Markovic M, Matthews D, May D, Enright J, Leontidis G (2022) The role of cross-silo federated learning in facilitating data sharing in the agri-food sector. Comput Electron Agric 193:106648","journal-title":"Comput Electron Agric"},{"key":"10774_CR18","first-page":"1","volume-title":"International conference on theory and applications of models of computation","author":"C Dwork","year":"2008","unstructured":"Dwork C (2008) Differential privacy: A survey of results. International conference on theory and applications of models of computation. Springer, Berlin, Heidelberg, pp 1\u201319"},{"issue":"2","key":"10774_CR19","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1109\/MWC.008.00475","volume":"29","author":"JQ Fan","year":"2022","unstructured":"Fan JQ, Wang XH, Guo YX, Hu XP, Hu B (2022) Federated learning driven secure internet of medical things. IEEE Wirel Commun 29(2):68\u201375","journal-title":"IEEE Wirel Commun"},{"issue":"18","key":"10774_CR20","doi-asserted-by":"publisher","first-page":"16048","DOI":"10.1109\/JIOT.2023.3267112","volume":"10","author":"MC Fan","year":"2023","unstructured":"Fan MC, Ji KL, Zhang ZF, Yu HF, Sun G (2023) Lightweight Privacy and Security Computing for Blockchained Federated Learning in IoT. IEEE Internet Things J 10(18):16048\u201316060","journal-title":"IEEE Internet Things J"},{"key":"10774_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.comcom.2022.01.002","volume":"186","author":"C Fang","year":"2022","unstructured":"Fang C, Guo YB, Ma JL, Xie HD, Wang YF (2022) A privacy-preserving and verifiable federated learning method based on blockchain. Comput Commun 186:1\u201311","journal-title":"Comput Commun"},{"key":"10774_CR22","unstructured":"Fang MH, Cao XY, Jia JY, Gong N (2020) Local model poisoning attacks to {Byzantine-Robust} federated learning. In: 29th USENIX Security Symposium (USENIX Security 20), pp. 1605\u20131622. USENIX, Boston, Massachusetts, USA"},{"issue":"5","key":"10774_CR23","doi-asserted-by":"publisher","first-page":"1092","DOI":"10.1109\/TC.2021.3072033","volume":"71","author":"L Feng","year":"2021","unstructured":"Feng L, Zhao Y, Guo S, Qiu X, Li W, Yu P (2021) BAFL: A Blockchain-Based Asynchronous Federated Learning Framework. IEEE Trans Comput 71(5):1092\u20131103","journal-title":"IEEE Trans Comput"},{"key":"10774_CR24","unstructured":"Fraboni Y, Vidal R, Lorenzi M (2021) Free-rider attacks on model aggregation in federated learning. In: Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, vol 130, pp. 1846\u20131854. PMLR, Buenos Aires, Argentina"},{"issue":"4","key":"10774_CR25","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1109\/JAS.2021.1003925","volume":"8","author":"O Friha","year":"2021","unstructured":"Friha O, Ferrag MA, Shu L, Maglaras L, Wang XC (2021) Internet of things for the future of smart agriculture: A comprehensive survey of emerging technologies. IEEE\/CAA J Automatica Sinica 8(4):718\u2013752","journal-title":"IEEE\/CAA J Automatica Sinica"},{"issue":"2","key":"10774_CR26","first-page":"359","volume":"10","author":"D Garg","year":"2023","unstructured":"Garg D, Alam M (2023) Smart agriculture: a literature review. J Management Anal 10(2):359\u2013415","journal-title":"J Management Anal"},{"key":"10774_CR27","doi-asserted-by":"publisher","first-page":"126897","DOI":"10.1016\/j.neucom.2023.126897","volume":"561","author":"LN Ge","year":"2023","unstructured":"Ge LN, Li HA, Wang X, Wang Z (2023) A review of secure federated learning: privacy leakage threats, protection technologies, challenges and future directions. Neurocomputing 561:126897","journal-title":"Neurocomputing"},{"key":"10774_CR28","unstructured":"Geyer R C, Klein T, Nabi M (2017) Differentially private FL: A client level perspective. arXiv preprint arXiv:1712.07557"},{"issue":"3","key":"10774_CR29","first-page":"2191","volume":"17","author":"AM Ghosh","year":"2020","unstructured":"Ghosh AM, Grolinger K (2020) Edge-cloud computing for internet of things data analytics: embedding intelligence in the edge with deep learning. IEEE Trans Industr Inf 17(3):2191\u20132200","journal-title":"IEEE Trans Industr Inf"},{"issue":"1","key":"10774_CR30","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1109\/MNET.011.2000783","volume":"36","author":"XL Gong","year":"2022","unstructured":"Gong XL, Chen YJ, Huang HY, Liao YQ, Wang S, Wang Q (2022) Coordinated Backdoor Attacks against Federated Learning with Model-Dependent Triggers. IEEE Network 36(1):84\u201390","journal-title":"IEEE Network"},{"issue":"7","key":"10774_CR31","doi-asserted-by":"publisher","first-page":"2057","DOI":"10.1109\/TMI.2022.3213244","volume":"42","author":"X Gong","year":"2023","unstructured":"Gong X, Song L, Vedula R, Sharma A, Zheng M, Planche B, Innanje A, Chen T, Yuan JS, Doermann D, Wu ZY (2023) Federated Learning with Privacy-Preserving Ensemble Attention Distillation. IEEE Trans Med Imaging 42(7):2057\u20132067","journal-title":"IEEE Trans Med Imaging"},{"key":"10774_CR32","unstructured":"Goodfellow I J, Shlens J, Szegedy C (2014) Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572"},{"issue":"10","key":"10774_CR33","doi-asserted-by":"publisher","first-page":"6526","DOI":"10.1109\/TII.2022.3156645","volume":"18","author":"JJ Guo","year":"2022","unstructured":"Guo JJ, Li HY, Huang FR, Liu ZQ, Peng YG, Li XH, Ma JF, Menon VG, Lgorevich KK (2022) ADFL: A poisoning attack defense framework for horizontal federated learning. IEEE Trans Industr Inf 18(10):6526\u20136536","journal-title":"IEEE Trans Industr Inf"},{"key":"10774_CR34","first-page":"1","volume":"2023","author":"W Guo","year":"2023","unstructured":"Guo W, Wang YJ, Chen X, Jiang PY (2023) Federated transfer learning for auxiliary classifier generative adversarial networks: framework and industrial application. J Intell Manuf 2023:1\u201316","journal-title":"J Intell Manuf"},{"issue":"10","key":"10774_CR35","doi-asserted-by":"publisher","first-page":"18273","DOI":"10.1109\/TITS.2022.3165513","volume":"23","author":"B Hammi","year":"2022","unstructured":"Hammi B, Idir YM, Zeadally S, Khatoun R, Nebhen J (2022) Is it really easy to detect sybil attacks in C-ITS environments: a position paper. IEEE Trans Intell Transp Syst 23(10):18273\u201318287","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"7","key":"10774_CR36","doi-asserted-by":"publisher","first-page":"2044","DOI":"10.1109\/TMI.2023.3239391","volume":"42","author":"A Hatamizadeh","year":"2023","unstructured":"Hatamizadeh A, Yin H, Molchanov P, Myronenko A, Li WQ, Dogra P, Feng A, Flores MG, Kautz J, Xu DG, Roth HR (2023) Do gradient inversion attacks make federated learning unsafe? IEEE Trans Med Imaging 42(7):2044\u20132056","journal-title":"IEEE Trans Med Imaging"},{"key":"10774_CR37","doi-asserted-by":"publisher","first-page":"109222","DOI":"10.1016\/j.sigpro.2023.109222","volume":"213","author":"X He","year":"2023","unstructured":"He X, Zhu H, Ling Q (2023) C-RSA: Byzantine-robust and communication-efficient distributed learning in the non-convex and non-IID regime. Signal Process 213:109222","journal-title":"Signal Process"},{"key":"10774_CR38","unstructured":"Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531"},{"key":"10774_CR39","doi-asserted-by":"crossref","unstructured":"Hitaj B, Ateniese G, Perez-Cruz F (2017) Deep models under the GAN: information leakage from collaborative deep learning. In: Proceedings of the 2017 ACM SIGSAC conference on computer and communications security, pp. 603\u2013618. ACM, Dallas, Texas, USA","DOI":"10.1145\/3133956.3134012"},{"issue":"22","key":"10774_CR40","doi-asserted-by":"publisher","first-page":"22158","DOI":"10.1109\/JIOT.2021.3090951","volume":"9","author":"J Hou","year":"2021","unstructured":"Hou J, Su M, Fu A, Yu Y (2021) Verifiable privacy-preserving scheme based on vertical federated random forest. IEEE Internet Things J 9(22):22158\u201322172","journal-title":"IEEE Internet Things J"},{"issue":"4","key":"10774_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3620667","volume":"56","author":"L Hu","year":"2023","unstructured":"Hu L, Yan AL, Yan HY, Huang T, Zhang YY, Dong CY, Yang CS (2023) Defenses to Membership Inference Attacks: A Survey. ACM Comput Surv 56(4):1\u201334","journal-title":"ACM Comput Surv"},{"key":"10774_CR42","unstructured":"Hu C, Jiang J, Wang Z (2019) Decentralized federated learning: A segmented gossip approach. arXiv preprint arXiv:1908. 07782"},{"issue":"9","key":"10774_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3555802","volume":"55","author":"HC Hua","year":"2023","unstructured":"Hua HC, Li YT, Wang TH, Dong NQ, Li W, Cao JW (2023) Edge computing with artificial intelligence: A machine learning perspective. ACM Comput Surv 55(9):1\u201335","journal-title":"ACM Comput Surv"},{"key":"10774_CR44","doi-asserted-by":"publisher","first-page":"109678","DOI":"10.1016\/j.comnet.2023.109678","volume":"226","author":"XH Huang","year":"2023","unstructured":"Huang XH, Han L, Li DD, Xie K, Zhang Y (2023a) A reliable and fair federated learning mechanism for mobile edge computing. Comput Netw 226:109678","journal-title":"Comput Netw"},{"key":"10774_CR45","unstructured":"Huang Y, Su Y, Ravi S, Song Z, Arora S, Li K (2020) Privacy-preserving learning via deep net pruning. arXiv preprint arXiv:2003.01876"},{"key":"10774_CR46","doi-asserted-by":"crossref","unstructured":"Huang HL, Shi W, Feng YH, Niu CY, Cheng GQ, Huang JC, Liu Z (2023) Active Client Selection for Clustered Federated Learning. IEEE Transactions on Neural Networks and Learning Systems 1\u201315 (Early Access)","DOI":"10.1109\/TNNLS.2023.3294295"},{"key":"10774_CR47","doi-asserted-by":"crossref","unstructured":"Ibitoye O, Shafiq M O, Matrawy A (2021) DiPSeN: Differentially Private Self-normalizing Neural Networks For Adversarial Robustness in FL. arXiv preprint arXiv:2101.03218","DOI":"10.1016\/j.cose.2022.102631"},{"key":"10774_CR48","doi-asserted-by":"publisher","first-page":"102076","DOI":"10.1016\/j.inffus.2023.102076","volume":"103","author":"MA Jan","year":"2023","unstructured":"Jan MA, Zhang W, Khan F, Abbas S, Khan R (2023) Lightweight and smart data fusion approaches for wearable devices of the Internet of Medical Things. Information Fusion 103:102076","journal-title":"Information Fusion"},{"key":"10774_CR49","doi-asserted-by":"publisher","first-page":"4417","DOI":"10.1109\/TIP.2022.3184255","volume":"31","author":"X Jia","year":"2022","unstructured":"Jia X, Zhang Y, Wu B, Wang J, Cao X (2022) Boosting fast AT with learnable adversarial initialization. IEEE Trans Image Process 31:4417\u20134430","journal-title":"IEEE Trans Image Process"},{"issue":"3","key":"10774_CR50","doi-asserted-by":"publisher","first-page":"440","DOI":"10.3390\/electronics9030440","volume":"9","author":"J Jiang","year":"2020","unstructured":"Jiang J, Hu L, Hu C, Liu J, Wang Z (2020) BACombo\u2014Bandwid-th-aware decentralized federated learning. Electronics 9(3):440\u2013455","journal-title":"Electronics"},{"key":"10774_CR51","doi-asserted-by":"publisher","first-page":"1625","DOI":"10.1109\/TIFS.2023.3249568","volume":"18","author":"Y Jiang","year":"2023","unstructured":"Jiang Y, Zhang W, Chen Y (2023) Data quality detection mechanism against label flipping attacks in federated learning. IEEE Trans Inf Forensics Secur 18:1625\u20131637","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"10774_CR52","first-page":"103580","volume":"77","author":"S Jin","year":"2023","unstructured":"Jin S, Li Y, Chen X, Li RX, Shen ZB (2023) Blockchain-based fairness-enhanced federated learning scheme against label flipping attack. J Inform Secur App 77:103580","journal-title":"J Inform Secur App"},{"issue":"4","key":"10774_CR53","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/MNET.007.2100717","volume":"36","author":"AP Kalapaaking","year":"2022","unstructured":"Kalapaaking AP, Stephanie V, Khalil I, Atiquzzaman M, Yi X, Almashor M (2022) SMPC-Based Federated Learning for 6G-Enabled Internet of Medical Things. IEEE Network 36(4):182\u2013189","journal-title":"IEEE Network"},{"key":"10774_CR54","unstructured":"Kilbertus N, Gasc\u00f3n A, Kusner M, Veale M, Gummadi K, Weller A (2018) Blind justice: Fairness with encrypted sensitive attributes. In: International Conference on Machine Learning, pp. 2630\u20132639. PMLR, Stockholm, Sweden"},{"issue":"4","key":"10774_CR55","first-page":"765","volume":"59","author":"XW Li","year":"2022","unstructured":"Li XW, Chen BH, Yang DQ, Wu GF (2022a) Review of Security Protocols in Edge Computing Environments. J Comp Res Develop 59(4):765\u2013780","journal-title":"J Comp Res Develop"},{"key":"10774_CR56","doi-asserted-by":"publisher","first-page":"3264","DOI":"10.1109\/TIFS.2022.3203316","volume":"17","author":"Q Li","year":"2022","unstructured":"Li Q, Wu J, Quan J, Shi J, Zhang S (2022b) Efficient Quantum Blockchain with a Consensus Mechanism QDPoS. IEEE Trans Inf Forensics Secur 17:3264\u20133276","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"10774_CR57","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.future.2023.02.021","volume":"144","author":"H Li","year":"2023","unstructured":"Li H, Li CC, Wang J, Yang AM, Ma ZZ, Zhang ZQ, Hua DB (2023a) Review on security of federated learning and its application in healthcare. Futur Gener Comput Syst 144:271\u2013290","journal-title":"Futur Gener Comput Syst"},{"key":"10774_CR58","doi-asserted-by":"publisher","first-page":"103319","DOI":"10.1016\/j.cose.2023.103319","volume":"132","author":"YL Li","year":"2023","unstructured":"Li YL, Yuan D, Sani AS, Bao W (2023b) Enhancing Federated Learning robustness in adversarial environment through clustering Non-IID features. Comput Secur 132:103319","journal-title":"Comput Secur"},{"key":"10774_CR59","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.future.2023.03.010","volume":"144","author":"DF Li","year":"2023","unstructured":"Li DF, Lai JH, Wang RJ, Li X, Vijayakumar P, Gupta BB, Alhalabi W (2023c) Ubiquitous intelligent federated learning privacy-preserving scheme under edge computing. Futur Gener Comput Syst 144:205\u2013218","journal-title":"Futur Gener Comput Syst"},{"key":"10774_CR60","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.future.2023.05.002","volume":"147","author":"BB Li","year":"2023","unstructured":"Li BB, Wang PR, Shao ZR, Liu A, Jiang YK (2023d) Defending Byzantine attacks in ensemble federated learning: A reputation-based phishing approach. Futur Gener Comput Syst 147:136\u2013148","journal-title":"Futur Gener Comput Syst"},{"key":"10774_CR61","doi-asserted-by":"publisher","first-page":"12399","DOI":"10.1007\/s10489-022-04111-0","volume":"53","author":"J Li","year":"2023","unstructured":"Li J, Yan T, Ren P (2023f) VFL-R: a novel framework for multi-party in vertical federated learning. Appl Intell 53:12399\u201312415","journal-title":"Appl Intell"},{"key":"10774_CR62","doi-asserted-by":"crossref","unstructured":"Li X, Chen B, Lu W (2022) FedDKD: Federated Learning with Decentralized Knowledge Distillation. arXiv preprint arXiv:2205.00706","DOI":"10.1007\/s10489-022-04431-1"},{"key":"10774_CR63","unstructured":"Li J, Rakin A S, Chen X, Yang L, He ZZ, Fan DL, Chakrabarti C (2023) Model Extraction Attacks on Split Federated Learning. arXiv preprint arXiv:2303.08581"},{"key":"10774_CR64","unstructured":"Lin J, Du M, Liu J (2019) Free-riders in federated learning: Attacks and defenses. arXiv preprint arXiv:1911.12560"},{"key":"10774_CR65","doi-asserted-by":"crossref","unstructured":"Lin W, Li B and Wang C, Towards Private Learning on Decentralized Graphs with Local Differential Privacy, IEEE Transactions on Information Forensics and Security 17: 2936\u20132946","DOI":"10.1109\/TIFS.2022.3198283"},{"issue":"8","key":"10774_CR66","doi-asserted-by":"publisher","first-page":"1754","DOI":"10.1109\/TPDS.2020.2975189","volume":"31","author":"W Liu","year":"2020","unstructured":"Liu W, Chen L, Chen Y, Zhang W (2020) Accelerating federated learning via momentum gradient descent. IEEE Trans Parallel Distrib Syst 31(8):1754\u20131766","journal-title":"IEEE Trans Parallel Distrib Syst"},{"issue":"21","key":"10774_CR67","doi-asserted-by":"publisher","first-page":"21025","DOI":"10.1109\/JIOT.2022.3176305","volume":"9","author":"Y Liu","year":"2022","unstructured":"Liu Y, Dong Y, Wang H, Jiang H, Xu Q (2022) Distributed fog computing and federated-learning-enabled secure aggregation for IoT devices. IEEE Internet Things J 9(21):21025\u201321037","journal-title":"IEEE Internet Things J"},{"key":"10774_CR68","doi-asserted-by":"publisher","first-page":"2398","DOI":"10.1109\/TIFS.2023.3266919","volume":"18","author":"Z Liu","year":"2023","unstructured":"Liu Z, Lin HY, Liu Y (2023a) Long-Term Privacy-Preserving Aggregation with User-Dynamics for Federated Learning. IEEE Trans Inf Forensics Secur 18:2398\u20132412","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"10774_CR69","doi-asserted-by":"publisher","first-page":"5860","DOI":"10.1109\/TIFS.2023.3309095","volume":"18","author":"HF Liu","year":"2023","unstructured":"Liu HF, Li B, Gao CL, Xie P, Zhao CL (2023b) Privacy-Encoded Federated Learning Against Gradient-Based Data Reconstruction Attacks. IEEE Trans Inf Forensics Secur 18:5860\u20135875","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"10774_CR70","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Alt A, Tromer E, Vaikuntanathan V (2012) On-the-fly multiparty computation on the cloud via multikey fully homomorphic encryption. In: Proceedings of the forty-fourth annual ACM symposium on Theory of computing, pp.1219\u20131234. ACM, New York, NY, USA","DOI":"10.1145\/2213977.2214086"},{"issue":"9","key":"10774_CR71","doi-asserted-by":"publisher","first-page":"5880","DOI":"10.1002\/int.22818","volume":"37","author":"J Ma","year":"2022","unstructured":"Ma J, Naas SA, Sigg S, Lyu X (2022a) Privacy-preserving FL based on multi-key homomorphic encryption. Int J Intell Syst 37(9):5880\u20135901","journal-title":"Int J Intell Syst"},{"key":"10774_CR72","doi-asserted-by":"publisher","first-page":"103561","DOI":"10.1016\/j.csi.2021.103561","volume":"80","author":"X Ma","year":"2022","unstructured":"Ma X, Zhou Y, Wang L, Miao M (2022b) Privacy-preserving byzantine-robust FL. Computer Standards & Interfaces 80:103561","journal-title":"Computer Standards & Interfaces"},{"key":"10774_CR73","doi-asserted-by":"crossref","unstructured":"Mayhoub S, M. Shami T (2023) A Review of Client Selection Methods in Federated Learning. Archives of Computational Methods in Engineering 1\u201324","DOI":"10.1007\/s11831-023-10011-4"},{"key":"10774_CR74","unstructured":"Mcmahan B, Moore E, Ramage D, Hampson S, Arcas BAY (2017) Communication-efficient learning of deep networks from decentralized data. In: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, pp. 1273\u20131282. PMLR, Ft Lauderdale, USA"},{"key":"10774_CR75","unstructured":"McMahan H B, Ramage D, Talwar K, Zhang L (2017) Learning differentially private recurrent language models. arXiv preprint arXiv:1710.06963"},{"key":"10774_CR76","unstructured":"Mejia F A, Gamble P, Hampel-Arias Z, Lomnitz M, Tindall L, Barrios MA (2019) Robust or Private? AT Makes Models More Vulnerable to Privacy Attacks. arXiv preprint arXiv: 1906.06449"},{"key":"10774_CR77","doi-asserted-by":"publisher","first-page":"2848","DOI":"10.1109\/TIFS.2022.3196274","volume":"17","author":"YB Miao","year":"2022","unstructured":"Miao YB, Liu ZT, Li HW, Choo KKR, Deng RH (2022) Privacy-Preserving Byzantine-Robust Federated Learning via Blockchain Systems. IEEE Trans Inf Forensics Secur 17:2848\u20132861","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"10774_CR78","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.comcom.2024.01.006","volume":"216","author":"T Muazu","year":"2024","unstructured":"Muazu T, Mao Y, Muhammad AU, Ibrahim M, Kumshe UMM, Samuel O (2024) A federated learning system with data fusion for healthcare using multi-party computation and additive secret sharing. Comput Commun 216:168\u2013182","journal-title":"Comput Commun"},{"issue":"16","key":"10774_CR79","doi-asserted-by":"publisher","first-page":"14418","DOI":"10.1109\/JIOT.2023.3263598","volume":"10","author":"R Myrzashova","year":"2023","unstructured":"Myrzashova R, Alsamhi SH, Shvetsov AV, Hawbani A, Wei X (2023) Blockchain meets federated learning in healthcare: A systematic review with challenges and opportunities. IEEE Internet Things J 10(16):14418\u201314437","journal-title":"IEEE Internet Things J"},{"key":"10774_CR80","doi-asserted-by":"publisher","first-page":"103723","DOI":"10.1016\/j.csi.2023.103723","volume":"86","author":"AK Nair","year":"2023","unstructured":"Nair AK, Raj ED, Sahoo J (2023) A robust analysis of adversarial attacks on federated learning environments. Computer Standards & Interfaces 86:103723","journal-title":"Computer Standards & Interfaces"},{"key":"10774_CR81","doi-asserted-by":"crossref","unstructured":"Narayanan A, Shmatikov V (2008) Robust de-anonymization of large sparse datasets. In: 2008 IEEE Symposium on Security and Privacy, pp. 111\u2013125. IEEE, Oakland, California, USA","DOI":"10.1109\/SP.2008.33"},{"key":"10774_CR82","doi-asserted-by":"publisher","first-page":"107166","DOI":"10.1016\/j.engappai.2023.107166","volume":"127","author":"TD Nguyen","year":"2024","unstructured":"Nguyen TD, Nguyen T, Nguyen PL, Pham HH, Doan KD, Wong K (2024) Backdoor attacks and defenses in federated learning: Survey, challenges and future research directions. Eng Appl Artif Intell 127:107166","journal-title":"Eng Appl Artif Intell"},{"key":"10774_CR83","doi-asserted-by":"publisher","first-page":"119784","DOI":"10.1016\/j.ins.2023.119784","volume":"653","author":"L Ni","year":"2023","unstructured":"Ni L, Gong X, Li JF, Tang YC, Luan Z, Zhang JQ (2023) rFedFW: Secure and Trustable Aggregation Scheme for Byzantine-Robust Federated Learning in Internet of Things. Inf Sci 653:119784","journal-title":"Inf Sci"},{"issue":"1","key":"10774_CR84","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3604933","volume":"56","author":"ZL Ning","year":"2023","unstructured":"Ning ZL, Hu H, Wang XJ, Guo L, Guo S, Wang GY, Gao XB (2023) Mobile Edge Computing and Machine Learning in The Internet of Unmanned Aerial Vehicles: A Survey. ACM Comput Surv 56(1):1\u201331","journal-title":"ACM Comput Surv"},{"key":"10774_CR85","doi-asserted-by":"crossref","unstructured":"Papernot N, McDaniel P, Sinha A, Wellman MP (2018) Sok: Security and privacy in machine learning. In: 2018 IEEE European Symposium on Security and Privacy, pp. 399\u2013414. IEEE, London, United Kingdom","DOI":"10.1109\/EuroSP.2018.00035"},{"issue":"5","key":"10774_CR86","doi-asserted-by":"publisher","first-page":"1333","DOI":"10.1109\/TIFS.2017.2787987","volume":"13","author":"LT Phong","year":"2017","unstructured":"Phong LT, Aono Y, Hayashi T, Wang LH, Moriai SH (2017) Privacy-preserving deep learning via additively homomorphic encryption. IEEE Trans Inf Forensics Secur 13(5):1333\u20131345","journal-title":"IEEE Trans Inf Forensics Secur"},{"key":"10774_CR87","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1016\/j.future.2023.09.008","volume":"150","author":"P Qi","year":"2023","unstructured":"Qi P, Chiaro D, Guzzo A, Ianni M, Fortino G, Piccialli F (2023) Model aggregation techniques in federated learning: A comprehensive survey. Futur Gener Comput Syst 150:272\u2013293","journal-title":"Futur Gener Comput Syst"},{"issue":"2","key":"10774_CR88","doi-asserted-by":"publisher","first-page":"1078","DOI":"10.1109\/COMST.2021.3062546","volume":"23","author":"P Ranaweera","year":"2021","unstructured":"Ranaweera P, Jurcut AD, Liyanage M (2021) Survey on multi-access edge computing security and privacy. IEEE Commun Surv Tutorials 23(2):1078\u20131124","journal-title":"IEEE Commun Surv Tutorials"},{"key":"10774_CR89","doi-asserted-by":"publisher","first-page":"102783","DOI":"10.1016\/j.cose.2022.102783","volume":"120","author":"MM Rashid","year":"2022","unstructured":"Rashid MM, Kamruzzaman J, Hassan MM, Lmam T, Wibowo S, Gordon S, Fortino G (2022) AT for Deep Learning-based Cyberattack Detection in IoT-based Smart City Applications. Comput Secur 120:102783","journal-title":"Comput Secur"},{"key":"10774_CR90","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.future.2022.03.003","volume":"133","author":"N Rodr\u00edguez-Barroso","year":"2022","unstructured":"Rodr\u00edguez-Barroso N, Mart\u00ednez-C\u00e1mara E, Luz\u00f3n MV, Herrera F (2022a) Dynamic defense against byzantine poisoning attacks in federated learning. Futur Gener Comput Syst 133:1\u20139","journal-title":"Futur Gener Comput Syst"},{"key":"10774_CR91","doi-asserted-by":"publisher","first-page":"108588","DOI":"10.1016\/j.knosys.2022.108588","volume":"245","author":"N Rodr\u00edguez-Barroso","year":"2022","unstructured":"Rodr\u00edguez-Barroso N, Mart\u00ednez-C\u00e1mara E, Luz\u00f3n MV, Herrera F (2022b) Backdoor attacks-resilient aggregation based on Robust Filtering of Outliers in federated learning for image classification. Knowl-Based Syst 245:108588","journal-title":"Knowl-Based Syst"},{"issue":"15","key":"10774_CR92","first-page":"1","volume":"52","author":"G Ryu","year":"2022","unstructured":"Ryu G, Choi D (2022) A hybrid AT for deep learning model and denoising network resistant to adversarial examples. Appl Intell 52(15):1\u201314","journal-title":"Appl Intell"},{"key":"10774_CR93","doi-asserted-by":"publisher","first-page":"105560","DOI":"10.1016\/j.engappai.2022.105560","volume":"117","author":"M Sepahvand","year":"2022","unstructured":"Sepahvand M, Abdali-Mohammadi F, Taherkordi A (2022) An adaptive teacher\u2013student learning algorithm with decomposed knowledge distillation for on-edge intelligence. Eng Appl Artif Intell 117:105560","journal-title":"Eng Appl Artif Intell"},{"key":"10774_CR94","doi-asserted-by":"publisher","first-page":"102691","DOI":"10.1016\/j.artmed.2023.102691","volume":"146","author":"S Sharma","year":"2023","unstructured":"Sharma S, Guleria K (2023) A comprehensive review on federated learning based models for healthcare applications. Artif Intell Med 146:102691","journal-title":"Artif Intell Med"},{"issue":"16","key":"10774_CR95","doi-asserted-by":"publisher","first-page":"14483","DOI":"10.1109\/JIOT.2023.3268705","volume":"10","author":"M Shen","year":"2023","unstructured":"Shen M, Gu A, Kang J, Tang XY, Lin XD, Zhu LH, Niyato D (2023) Blockchains for Artificial Intelligence of Things: A Comprehensive Survey. IEEE Internet Things J 10(16):14483\u201314506","journal-title":"IEEE Internet Things J"},{"issue":"5","key":"10774_CR96","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1109\/JIOT.2016.2579198","volume":"3","author":"W Shi","year":"2016","unstructured":"Shi W, Cao J, Zhang Q, Li YHZ, Xu LY (2016) Edge computing: Vision and challenges. IEEE Internet Things J 3(5):637\u2013646","journal-title":"IEEE Internet Things J"},{"issue":"1","key":"10774_CR97","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1109\/JPROC.2022.3226481","volume":"111","author":"MMH Shuvo","year":"2022","unstructured":"Shuvo MMH, Islam SK, Cheng JL, Morshed BI (2022) Efficient acceleration of deep learning inference on resource-constrained edge devices: A review. Proc IEEE 111(1):42\u201391","journal-title":"Proc IEEE"},{"key":"10774_CR98","doi-asserted-by":"crossref","unstructured":"Singh A (2006) Eclipse attacks on overlay networks: Threats and defenses. In: Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications, IEEE, Barcelona, Catalunya, SPAIN","DOI":"10.1109\/INFOCOM.2006.231"},{"key":"10774_CR99","doi-asserted-by":"crossref","unstructured":"Song L, Shokri R, Mittal P (2019) Privacy risks of securing machine learning models against adversarial examples. In: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pp. 241\u2013257. ACM, London, United Kingdom","DOI":"10.1145\/3319535.3354211"},{"key":"10774_CR100","unstructured":"Tang M, Zhang J, Ma M, Divalentin L, Ding A, Hassanzadeh A, Li H, Chen Y (2022) FADE: Enabling Large-Scale Federated AT on R-esource-Constrained Edge Devices. arXiv preprint arXiv:2209.03839"},{"key":"10774_CR101","first-page":"1633","volume":"33","author":"F Tramer","year":"2020","unstructured":"Tramer F, Carlini N, Brendel W, Madry A (2020) On adaptive attacks to adversarial example defenses. Adv Neural Inf Process Syst 33:1633\u20131645","journal-title":"Adv Neural Inf Process Syst"},{"key":"10774_CR102","unstructured":"Tram\u00e8r F, Zhang F, Juels A, Reiter MK, Ristenpart T (2016) Stealing machine learning models via prediction {APIs}. In: 25th USENIX security symposium (USENIX Security 16), pp. 601\u2013618. USENIX, Austin, TX, USA"},{"key":"10774_CR103","unstructured":"Tram\u00e8r F, Kurakin A, Papernot N, Goodfellow L, Boneh D, Mcdaniel P (2017) Ensemble Adversarial Training: Attacks and defenses. arXiv preprint arXiv:1705.07204"},{"key":"10774_CR104","doi-asserted-by":"publisher","first-page":"105384","DOI":"10.1016\/j.engappai.2022.105384","volume":"116","author":"FY Wan","year":"2022","unstructured":"Wan FY, Ma T, Hua Y, Liao B, Qing XL (2022) Secure distributed estimation under Byzantine attack and manipulation attack. Eng Appl Artif Intell 116:105384","journal-title":"Eng Appl Artif Intell"},{"issue":"1","key":"10774_CR105","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1109\/TCSS.2019.2950017","volume":"7","author":"Y Wang","year":"2019","unstructured":"Wang Y, Gu M, Ma J, Jin Q (2019a) DNN-DP: Differential privacy enabled deep neural network learning framework for sensitive crowdsourcing data. IEEE Transact Comput Soc Systems 7(1):215\u2013224","journal-title":"IEEE Transact Comput Soc Systems"},{"issue":"2","key":"10774_CR106","doi-asserted-by":"publisher","first-page":"854","DOI":"10.1109\/JBHI.2022.3157725","volume":"27","author":"R Wang","year":"2022","unstructured":"Wang R, Lai J, Zhang Z, Li X, Vijayakumar P, Karuppiah M (2022a) Privacy-preserving federated learning for internet of medical things under edge computing. IEEE J Biomed Health Inform 27(2):854\u2013865","journal-title":"IEEE J Biomed Health Inform"},{"key":"10774_CR107","first-page":"1","volume":"60","author":"F Wang","year":"2022","unstructured":"Wang F, Wu X, Wang H (2022b) Seismic horizon identification using semi-supervised learning with virtual AT. IEEE Trans Geosci Remote Sens 60:1\u201311","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"10774_CR108","doi-asserted-by":"publisher","first-page":"109206","DOI":"10.1016\/j.comnet.2022.109206","volume":"215","author":"WL Wang","year":"2022","unstructured":"Wang WL, Wang YJ, Huang Y, Mu CC, Sun ZC, Tong XR, Cai ZP (2022c) Privacy protection federated learning system based on blockchain and edge computing in mobile crowdsourcing. Comput Netw 215:109206","journal-title":"Comput Netw"},{"issue":"9","key":"10774_CR109","doi-asserted-by":"publisher","first-page":"6092","DOI":"10.1109\/TWC.2023.3239400","volume":"22","author":"Z Wang","year":"2023","unstructured":"Wang Z, Zhou Y, Zou Y, Bennis M (2023) A graph neural network learning approach to optimize ris-assisted federated learning. IEEE Trans Wireless Commun 22(9):6092\u20136106","journal-title":"IEEE Trans Wireless Commun"},{"key":"10774_CR110","doi-asserted-by":"crossref","unstructured":"Wang Z, Song M, Zhang Z, Song Y, Wang Q, Qi H (2019) Beyond inferring class representatives: User-level privacy leakage from FL. In: IEEE INFOCOM 2019-IEEE Conference on Computer Communications, pp. 2512\u20132520. IEEE, Paris, France","DOI":"10.1109\/INFOCOM.2019.8737416"},{"key":"10774_CR111","unstructured":"Wang Y, Fan W, Yang K, Alhusaini N, Li J (2022) A Knowledge Distillation-Based Backdoor Attack in Federated Learning. arXiv preprint arXiv:2208.06176"},{"issue":"23","key":"10774_CR112","doi-asserted-by":"publisher","first-page":"20884","DOI":"10.1109\/JIOT.2023.3283855","volume":"10","author":"O Wehbi","year":"2023","unstructured":"Wehbi O, Arisdakessian S, Wahab OA, Otrok H, Otoum S, Mourad A, Guizani M (2023) FedMint: Intelligent Bilateral Client Selection in Federated Learning with Newcomer IoT Devices. IEEE Internet Things J 10(23):20884\u201320898","journal-title":"IEEE Internet Things J"},{"issue":"1","key":"10774_CR113","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1038\/s41467-021-27738-y","volume":"13","author":"C Wu","year":"2022","unstructured":"Wu C, Wu F, Lyu L, Huang Y, Xie X (2022) Communication-efficient federated learning via knowledge distillation. Nat Commun 13(1):3\u20138","journal-title":"Nat Commun"},{"issue":"3","key":"10774_CR114","doi-asserted-by":"publisher","first-page":"314","DOI":"10.3390\/e22030314","volume":"22","author":"P Xiao","year":"2020","unstructured":"Xiao P, Cheng S, Stankovic V, Vukobratovic D (2020) Averaging is probably not the optimum way of aggregating parameters in federated learning. Entropy 22(3):314\u2013325","journal-title":"Entropy"},{"issue":"3","key":"10774_CR115","doi-asserted-by":"publisher","first-page":"2608","DOI":"10.1109\/TII.2022.3172310","volume":"19","author":"X Xiao","year":"2022","unstructured":"Xiao X, Tang Z, Li CY, Xiao B, Li KL (2022) SCA: sybil-based collusion attacks of IIoT data poisoning in federated learning. IEEE Trans Industr Inf 19(3):2608\u20132618","journal-title":"IEEE Trans Industr Inf"},{"issue":"5","key":"10774_CR116","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3523061","volume":"13","author":"L Xie","year":"2022","unstructured":"Xie L, Liu J, Lu S, Chang TH, Shi Q (2022) An efficient learning framework for federated XGBoost using secret sharing and distributed optimization. ACM Transact Intell Systems Technol (TIST) 13(5):1\u201328","journal-title":"ACM Transact Intell Systems Technol (TIST)"},{"key":"10774_CR117","doi-asserted-by":"publisher","first-page":"100595","DOI":"10.1016\/j.cosrev.2023.100595","volume":"50","author":"CH Xu","year":"2023","unstructured":"Xu CH, Qu YY, Xiang Y, Gao LX (2023) Asynchronous federated learning on heterogeneous devices: A survey. Computer Sci Rev 50:100595","journal-title":"Computer Sci Rev"},{"key":"10774_CR118","doi-asserted-by":"publisher","first-page":"119743","DOI":"10.1016\/j.ins.2023.119743","volume":"651","author":"DS Yang","year":"2023","unstructured":"Yang DS, Luo SL, Zhou JJ, Pan LM, Yang XN, Xing JY (2023a) Efficient and persistent backdoor attack by boundary trigger set constructing against federated learning. Inf Sci 651:119743","journal-title":"Inf Sci"},{"issue":"4","key":"10774_CR119","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1109\/JSAC.2023.3242720","volume":"41","author":"J Yang","year":"2023","unstructured":"Yang J, Liu Y, Kassab R (2023b) Client Selection for Federated Bayesian Learning. IEEE J Sel Areas Commun 41(4):915\u2013928","journal-title":"IEEE J Sel Areas Commun"},{"key":"10774_CR120","doi-asserted-by":"crossref","unstructured":"Yang F, Qiao Y, Abedin MZ, Huang C (2022) Privacy-Preserved Credit Data Sharing Integrating Blockchain And Federated Learning For Industrial 4.0. IEEE Transactions on Industrial Informatics 18(12): 8755\u20138764","DOI":"10.1109\/TII.2022.3151917"},{"key":"10774_CR121","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109065","volume":"134","author":"C Yu","year":"2022","unstructured":"Yu C, Zhang Z, Li H, Sun J, Xu Z (2022a) Meta-learning-based AT for deep 3D face recognition on point clouds. Pattern Recogn 134:109065","journal-title":"Pattern Recogn"},{"key":"10774_CR122","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.comcom.2022.09.008","volume":"196","author":"F Yu","year":"2022","unstructured":"Yu F, Lin H, Wang X, Yassine A, Hossain MS (2022b) Blockchain-empowered secure federated learning system: Architecture and applications. Comput Commun 196:55\u201365","journal-title":"Comput Commun"},{"issue":"10","key":"10774_CR123","doi-asserted-by":"publisher","first-page":"10181","DOI":"10.1109\/TKDE.2023.3269017","volume":"35","author":"F Zhang","year":"2023","unstructured":"Zhang F, Wu RF, Guan JW, Zheng Z, Guo XG, Zhang X, Du XY, Shen XP (2023a) Expanding the Edge: Enabling Efficient Winograd CNN Inference with Deep Reuse on Edge Device. IEEE Trans Knowl Data Eng 35(10):10181\u201310196","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10774_CR124","doi-asserted-by":"publisher","first-page":"3289","DOI":"10.1109\/TIFS.2023.3280032","volume":"18","author":"Z Zhang","year":"2023","unstructured":"Zhang Z, Li J, Yu S, Makaya C (2023b) SAFELearning: Secure Aggregation in Federated Learning with Backdoor Detectability. IEEE Trans Inf Forensics Secur 18:3289\u20133304","journal-title":"IEEE Trans Inf Forensics Secur"},{"issue":"4","key":"10774_CR125","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1016\/j.dcan.2022.05.010","volume":"9","author":"JL Zhang","year":"2023","unstructured":"Zhang JL, Liu Y, Wu D, Lou S, Chen B, Yu S (2023d) VPFL: A verifiable privacy-preserving FL scheme for edge computing systems. Digital Communications and Networks 9(4):981\u2013989","journal-title":"Digital Communications and Networks"},{"key":"10774_CR126","unstructured":"Zhang J, Chen Y, Li H (2022) Privacy Leakage of AT Models in FL Systems. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.108\u2013114. IEEE, New Orleans, LA, USA"},{"key":"10774_CR127","unstructured":"Zhang X, Liu Q, Ba Z, Hong Y, Zheng T, Lin F, Lu L, Ren K (2023) Fltracer: Accurate poisoning attack provenance in federated learning.\u00a0arXiv preprint arXiv:2310.13424."},{"key":"10774_CR128","doi-asserted-by":"publisher","first-page":"3344862","DOI":"10.1155\/2021\/3344862","volume":"2021","author":"JZ Zhao","year":"2021","unstructured":"Zhao JZ, Mao KM, Huang CX, Zeng YY (2021) Utility Optimization of FL with Differential Privacy. Discret Dyn Nat Soc 2021:3344862","journal-title":"Discret Dyn Nat Soc"},{"key":"10774_CR129","doi-asserted-by":"publisher","first-page":"2059","DOI":"10.1109\/TIFS.2022.3176191","volume":"17","author":"J Zhao","year":"2022","unstructured":"Zhao J, Zhu H, Wang F, Lu R, Liu Z, Li H (2022) PVD-FL: A privacy-preserving and verifiable decentralized federated learning framework. IEEE Trans Inf Forensics Secur 17:2059\u20132073","journal-title":"IEEE Trans Inf Forensics Secur"},{"issue":"29","key":"10774_CR130","doi-asserted-by":"publisher","first-page":"21499","DOI":"10.1007\/s00521-023-08375-6","volume":"35","author":"W Zheng","year":"2023","unstructured":"Zheng W, Cao Y, Tan H (2023) Secure sharing of industrial IoT data based on distributed trust management and trusted execution environments: a federated learning approach. Neural Comput Appl 35(29):21499\u201321509","journal-title":"Neural Comput Appl"},{"key":"10774_CR131","first-page":"1","volume":"2021","author":"Z Zhou","year":"2021","unstructured":"Zhou Z, Tian Y, Peng C (2021) Privacy-preserving federated learning framework with general aggregation and multiparty entity matching. Wirel Commun Mob Comput 2021:1\u201314","journal-title":"Wirel Commun Mob Comput"},{"issue":"11","key":"10774_CR132","first-page":"1","volume":"111","author":"X Zhou","year":"2022","unstructured":"Zhou X, Tsang IW, Yin J (2022) LADDER: Latent boundary-guided adversarial training. Mach Learn 111(11):1\u201329","journal-title":"Mach Learn"},{"issue":"11","key":"10774_CR133","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3570953","volume":"55","author":"JC Zhu","year":"2022","unstructured":"Zhu JC, Cao JN, Saxena D, Jiang S, Ferradi, (2022) Blockchain-empowered Federated Learning: Challenges, Solutions, and Future Directions. ACM Comput Surv 55(11):1\u201331","journal-title":"ACM Comput Surv"},{"issue":"11","key":"10774_CR134","doi-asserted-by":"publisher","first-page":"13396","DOI":"10.1109\/TITS.2023.3243088","volume":"24","author":"RB Zhu","year":"2023","unstructured":"Zhu RB, Li MY, Yin JJ, Sun LB, Liu H (2023) Enhanced Federated Learning for Edge Data Security in Intelligent Transportation Systems. IEEE Trans Intell Transp Syst 24(11):13396\u201313408","journal-title":"IEEE Trans Intell Transp Syst"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-024-10774-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-024-10774-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-024-10774-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,17]],"date-time":"2024-05-17T02:19:13Z","timestamp":1715912353000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-024-10774-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,29]]},"references-count":134,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["10774"],"URL":"https:\/\/doi.org\/10.1007\/s10462-024-10774-7","relation":{},"ISSN":["1573-7462"],"issn-type":[{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,29]]},"assertion":[{"value":"24 April 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 April 2024","order":2,"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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"130"}}