{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T04:31:24Z","timestamp":1747283484324,"version":"3.37.3"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T00:00:00Z","timestamp":1708646400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T00:00:00Z","timestamp":1708646400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Chongqing University of Posts and Telecommunications Education  Research  Project","award":["XJG21248"],"award-info":[{"award-number":["XJG21248"]}]},{"DOI":"10.13039\/501100013223","name":"Chongqing Research Program of Basic Research and Frontier Technology","doi-asserted-by":"publisher","award":["Grant No. cstc2021jcyj-msxmX0530"],"award-info":[{"award-number":["Grant No. cstc2021jcyj-msxmX0530"]}],"id":[{"id":"10.13039\/501100013223","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Humanities and Social Sciences Fund of the Ministry of Education","award":["20YJCZH047"],"award-info":[{"award-number":["20YJCZH047"]}]},{"name":"Chongqing University of Posts and Telecommunications Japan Research Center Commissioned Project","award":["K2020-222"],"award-info":[{"award-number":["K2020-222"]}]},{"name":"Technology Innovation and Application Development Projects of Chongqing","award":["cstc2021jscx-gksbX0029"],"award-info":[{"award-number":["cstc2021jscx-gksbX0029"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Federated learning (FL) is a decentralized and privacy-preserving machine learning technique that protects data privacy by learning models locally and not sharing datasets. However, due to limited computing resources on devices and highly heterogeneous data in practical situations, the training efficiency and resource utilization of federated learning is low. In order to resolve these challenges, we introduce a blockchain-assisted dynamic adaptive and personalized federated learning framework (TV-FedAvg) in the presence of restricted computing power resources and data heterogeneity. After each round of local training, we utilize an improved scoring model based on VIKOR and TOPSIS to comprehensively score the devices. The scores are then utilized to choose devices for participation in global aggregation and to carry out model aggregation through blockchain consensus. Furthermore, resources are reallocated for the next round to enhance resource efficiency, model fairness, and performance. Finally, we demonstrate through experimentation that TV-FedAvg outperforms other models such as pFedMe, FedAvg, Per-FedAvg, and TOPSIS in terms of both efficiency and performance.<\/jats:p>","DOI":"10.1007\/s11063-024-11493-4","type":"journal-article","created":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T04:37:01Z","timestamp":1708663021000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Dynamic Adaptive and Resource-Allocated Selection Method Based on TOPSIS and VIKOR in Federated Learning"],"prefix":"10.1007","volume":"56","author":[{"given":"Lin","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuyu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiangping","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengcheng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,23]]},"reference":[{"issue":"5","key":"11493_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.106854","volume":"149","author":"L Li","year":"2020","unstructured":"Li L, Fan Y, Tse M et al (2020) A review of applications in federated learning. Comput Ind Eng 149(5):106854","journal-title":"Comput Ind Eng"},{"issue":"1","key":"11493_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.106775","volume":"216","author":"C Zhang","year":"2021","unstructured":"Zhang C, Xie Y, Bai H et al (2021) A survey on federated learning. Knowl-Based Syst 216(1):106775","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"11493_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000083","volume":"14","author":"EBP Kairouz","year":"2021","unstructured":"Kairouz EBP, Mcmahan HB (2021) Advances and open problems in federated learning. Found Trends Mach Learn 14(1):1\u2013210","journal-title":"Found Trends Mach Learn"},{"issue":"3","key":"11493_CR4","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","volume":"37","author":"T Li","year":"2020","unstructured":"Li T, Sahu AK, Talwalkar A, Smith V (2020) Federated learning: challenges, methods, and future directions. IEEE Signal Process Mag 37(3):50\u201360. https:\/\/doi.org\/10.1109\/MSP.2020.2975749","journal-title":"IEEE Signal Process Mag"},{"key":"11493_CR5","doi-asserted-by":"publisher","unstructured":"Li T, Sanjabi M, Beirami A et\u00a0al (2019) Fair resource allocation in federated learning. https:\/\/doi.org\/10.48550\/arXiv.1905.10497","DOI":"10.48550\/arXiv.1905.10497"},{"key":"11493_CR6","doi-asserted-by":"crossref","unstructured":"Song R, Liu D, Chen DZ et\u00a0al (2022) Federated learning via decentralized dataset distillation in resource-constrained edge environments. arXiv preprint arXiv:2208.11311","DOI":"10.1109\/IJCNN54540.2023.10191879"},{"issue":"12","key":"11493_CR7","doi-asserted-by":"publisher","first-page":"4282","DOI":"10.1109\/TPDS.2022.3187365","volume":"33","author":"A Sultana","year":"2022","unstructured":"Sultana A, Haque MM, Chen L et al (2022) Eiffel: efficient and fair scheduling in adaptive federated learning. IEEE Trans Parallel Distrib Syst 33(12):4282\u20134294","journal-title":"IEEE Trans Parallel Distrib Syst"},{"issue":"2","key":"11493_CR8","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1108\/IJICC-05-2022-0126","volume":"16","author":"M Xie","year":"2023","unstructured":"Xie M, Liu J, Chen S, Lin M (2023) A survey on blockchain consensus mechanism: research overview, current advances and future directions. Int J Intell Comput Cybern 16(2):314\u2013340","journal-title":"Int J Intell Comput Cybern"},{"issue":"8","key":"11493_CR9","first-page":"33","volume":"56","author":"X Zehui","year":"2017","unstructured":"Zehui X, Yang Z, Dusit N, Ping W, Zhu H (2017) When mobile blockchain meets edge computing. IEEE Commun Mag 56(8):33\u201339","journal-title":"IEEE Commun Mag"},{"key":"11493_CR10","first-page":"1","volume":"99","author":"X Qu","year":"2021","unstructured":"Qu X, Wang S, Hu Q, Cheng X (2021) Proof of federated learning: a novel energy-recycling consensus algorithm. IEEE Trans Parallel Distrib Syst 99:1\u20131","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"11493_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TC.2021.3117127","volume":"99","author":"L Feng","year":"2021","unstructured":"Feng L, Zhao Y, Guo S, Qiu X, Yu P (2021) Blockchain-based asynchronous federated learning for internet of things. IEEE Trans Comput 99:1\u20131","journal-title":"IEEE Trans Comput"},{"key":"11493_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TPDS.2020.3040981","volume":"99","author":"MP Uddin","year":"2020","unstructured":"Uddin MP, Xiang Y, Lu X, Yearwood J, Gao L (2020) Mutual information driven federated learning. IEEE Trans Parallel Distrib Syst 99:1\u20131","journal-title":"IEEE Trans Parallel Distrib Syst"},{"issue":"10","key":"11493_CR13","doi-asserted-by":"publisher","first-page":"2401","DOI":"10.1109\/TPDS.2021.3138848","volume":"33","author":"J Li","year":"2021","unstructured":"Li J, Shao Y, Wei K et al (2021) Blockchain assisted decentralized federated learning (BLADE-FL): performance analysis and resource allocation. IEEE Trans Parallel Distrib Syst 33(10):2401\u20132415","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"11493_CR14","doi-asserted-by":"publisher","unstructured":"Chakraborty S (2023) TOPSIS and modified TOPSIS: a comparative analysis. Decis Anal J. https:\/\/doi.org\/10.31224\/osf.io\/y39j7","DOI":"10.31224\/osf.io\/y39j7"},{"key":"11493_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.115224","author":"A Hashemi","year":"2021","unstructured":"Hashemi A, Dowlatshahi MB, Nezamabadi-Pour H (2021) VMFS: a VIKOR-based multi-target feature selection. Expert Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2021.115224","journal-title":"Expert Syst Appl"},{"key":"11493_CR16","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2022.3227014","author":"M Hu","year":"2022","unstructured":"Hu M et al (2022) AutoFL: a Bayesian game approach for autonomous client participation in federated edge learning. IEEE Trans Mob Comput. https:\/\/doi.org\/10.1109\/TMC.2022.3227014","journal-title":"IEEE Trans Mob Comput"},{"issue":"1","key":"11493_CR17","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1109\/TPDS.2020.3009406","volume":"32","author":"M Duan","year":"2021","unstructured":"Duan M, Liu D, Chen X, Liu R, Tan Y, Liang L (2021) 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":"11493_CR18","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2020.3045266","author":"Q Wu","year":"2020","unstructured":"Wu Q, Chen X, Zhou Z, Zhang J (2020) FedHome: cloud-edge based personalized federated learning for in-home health monitoring. IEEE Trans Mob Comput. https:\/\/doi.org\/10.1109\/TMC.2020.3045266","journal-title":"IEEE Trans Mob Comput"},{"key":"11493_CR19","doi-asserted-by":"publisher","first-page":"1698","DOI":"10.1109\/INFOCOM41043.2020.9155494","volume-title":"IEEE INFOCOM 2020\u2013IEEE conference on computer communications","author":"H Wang","year":"2020","unstructured":"Wang H, Kaplan Z, Niu D, Li B (2020) Optimizing federated learning on non-IID data with reinforcement learning. IEEE INFOCOM 2020\u2013IEEE conference on computer communications. ON, Canada, Toronto, pp 1698\u20131707"},{"key":"11493_CR20","first-page":"2174","volume":"2021","author":"M Yang","year":"2021","unstructured":"Yang M, Wang X, Zhu H, Wang H, Qian H (2021) Federated learning with class imbalance reduction. IEEE EUSIPCO 2021:2174\u20132178","journal-title":"IEEE EUSIPCO"},{"issue":"2","key":"11493_CR21","doi-asserted-by":"publisher","first-page":"170","DOI":"10.3390\/axioms12020170","volume":"12","author":"Ma Chuang","year":"2023","unstructured":"Chuang Ma, Xin Ren, Guangxia Xu, Bo He (2023) FedGR: federated graph neural network for recommendation systems. Axioms 12(2):170","journal-title":"Axioms"},{"key":"11493_CR22","unstructured":"Li T, Sahu AK, Zaheer M et\u00a0al (2018) Federated optimization in heterogeneous networks. arXiv:1812.06127"},{"key":"11493_CR23","doi-asserted-by":"crossref","unstructured":"Yao X, Sun L (2020) Continual Local Training For Better Initialization of Federated Models. In: IEEE international conference on image processing (ICIP), Abu Dhabi, United Arab Emirates, 2020, pp 1736\u20131740","DOI":"10.1109\/ICIP40778.2020.9190968"},{"key":"11493_CR24","unstructured":"Karimireddy SP, Kale S, Mohri M, Reddi S, Stich Suresh AT (2020) Scaffold: stochastic controlled averaging for federated learning. In: International conference on machine learning, pp. 5132\u20135143"},{"key":"11493_CR25","doi-asserted-by":"crossref","unstructured":"Nishio T, Yonetani R (2019) Client selection for federated learning with heterogeneous resources in mobile edge. In: Proc. IEEE international conference on communications, pp. 1\u20137","DOI":"10.1109\/ICC.2019.8761315"},{"key":"11493_CR26","first-page":"2598","volume-title":"Update aware device scheduling for federated learning at the wireless edge","author":"MM Amiri","year":"2020","unstructured":"Amiri MM, Deniz G, Kulkarni SR, Poor HV (2020) Update aware device scheduling for federated learning at the wireless edge. Proc. In, IEEE international symposium on information, pp 2598\u20132603"},{"issue":"5","key":"11493_CR27","doi-asserted-by":"publisher","first-page":"3357","DOI":"10.1007\/s11276-021-02643-w","volume":"27","author":"HC Ke","year":"2021","unstructured":"Ke HC, Wang H, Zhao HW, Sun WJ (2021) Deep reinforcement learning-based computation offloading and resource allocation in security-aware mobile edge computing. Wirel Netw 27(5):3357\u20133373","journal-title":"Wirel Netw"},{"key":"11493_CR28","doi-asserted-by":"publisher","unstructured":"Dinh CT, Tran NH, Nguyen TD (2020) Personalized federated learning with moreau envelopes. 2020.https:\/\/doi.org\/10.48550\/arXiv.2006.08848","DOI":"10.48550\/arXiv.2006.08848"},{"key":"11493_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/s11276-020-02409-w","author":"Q Liu","year":"2020","unstructured":"Liu Q, Mo R, Xu X et al (2020) Multi-objective resource allocation in mobile edge computing using PAES for Internet of Things. Wireless Networks. https:\/\/doi.org\/10.1007\/s11276-020-02409-w","journal-title":"Wireless Networks"},{"issue":"7","key":"11493_CR30","doi-asserted-by":"publisher","first-page":"1684","DOI":"10.1108\/IMDS-04-2020-0199","volume":"121","author":"H Lau","year":"2021","unstructured":"Lau H, Tsang YP, Nakandala D et al (2021) Risk quantification in cold chain management: a federated learning-enabled multi-criteria decision-making methodology. Ind Manag Data Syst 121(7):1684\u20131703","journal-title":"Ind Manag Data Syst"},{"issue":"2","key":"11493_CR31","doi-asserted-by":"publisher","first-page":"878","DOI":"10.1109\/JBHI.2022.3167256","volume":"27","author":"A Alamleh","year":"2023","unstructured":"Alamleh A et al (2023) Federated learning for IoMT applications: a standardization and benchmarking framework of intrusion detection systems. IEEE J Biomed Health Inform 27(2):878\u2013887","journal-title":"IEEE J Biomed Health Inform"},{"key":"11493_CR32","first-page":"1","volume":"99","author":"G Xu","year":"2019","unstructured":"Xu G, Liu Y, Khan PW (2019) Improvement of the DPoS consensus mechanism in blockchain based on vague sets. IEEE Trans Ind Inform 99:1\u20131","journal-title":"IEEE Trans Ind Inform"},{"key":"11493_CR33","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.ins.2023.01.049","volume":"626","author":"J Liu","year":"2023","unstructured":"Liu J, Xie MY, Chen SY, Xu GX, Wu TS, Li W (2023) TS-REPLICA: a novel replica placement algorithm based on the entropy weight TOPSIS method in spark for multimedia data analysis. Inf Sci 626:133\u2013148","journal-title":"Inf Sci"},{"issue":"3","key":"11493_CR34","doi-asserted-by":"publisher","first-page":"8363","DOI":"10.1007\/s11356-022-24493-5","volume":"30","author":"H Yang","year":"2023","unstructured":"Yang H, Zhang X, Fu K et al (2023) Comprehensive evaluation of urban water supply security based on the VIKOR-TOPSIS method. Environ Sci Pollut Res 30(3):8363\u20138375","journal-title":"Environ Sci Pollut Res"},{"issue":"2","key":"11493_CR35","doi-asserted-by":"publisher","first-page":"1507","DOI":"10.1007\/s40747-022-00857-9","volume":"9","author":"M Xie","year":"2023","unstructured":"Xie M, Liu J, Chen S et al (2023) Primary node election based on probabilistic linguistic term set with confidence interval in the PBFT consensus mechanism for blockchain. Complex Intell Syst 9(2):1507\u20131524","journal-title":"Complex Intell Syst"},{"issue":"6","key":"11493_CR36","doi-asserted-by":"publisher","first-page":"2271","DOI":"10.1109\/TMC.2020.3034479","volume":"21","author":"X Deng","year":"2020","unstructured":"Deng X, Li J, Shi L et al (2020) Wireless powered mobile edge computing: dynamic resource allocation and throughput maximization. IEEE Trans Mob Comput 21(6):2271\u20132288","journal-title":"IEEE Trans Mob Comput"},{"key":"11493_CR37","doi-asserted-by":"crossref","unstructured":"Fang M, Liu J (2020) Toward low-cost and stable blockchain networks. In: IEEE international conference on communications (ICC). IEEE, pp 1\u20136","DOI":"10.1109\/ICC40277.2020.9148615"},{"key":"11493_CR38","doi-asserted-by":"publisher","unstructured":"Fallah A, Mokhtari A, Ozdaglar A (2020) personalized federated learning: a meta-learning approach. https:\/\/doi.org\/10.48550\/arXiv.2002.07948","DOI":"10.48550\/arXiv.2002.07948"},{"issue":"12","key":"11493_CR39","doi-asserted-by":"publisher","first-page":"2743","DOI":"10.1109\/TMC.2019.2936202","volume":"19","author":"H Huang","year":"2020","unstructured":"Huang H, Guo S, Liang W, Wang K, Okabe Y (2020) Coflow-like online data acquisition from low-earth-orbit datacenters. IEEE Trans Mobile Comput 19(12):2743\u20132760","journal-title":"IEEE Trans Mobile Comput"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-024-11493-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-024-11493-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-024-11493-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T20:26:30Z","timestamp":1715891190000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-024-11493-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,23]]},"references-count":39,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["11493"],"URL":"https:\/\/doi.org\/10.1007\/s11063-024-11493-4","relation":{},"ISSN":["1573-773X"],"issn-type":[{"type":"electronic","value":"1573-773X"}],"subject":[],"published":{"date-parts":[[2024,2,23]]},"assertion":[{"value":"18 December 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 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":"Conflict of interest"}}],"article-number":"67"}}