{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:40:05Z","timestamp":1774917605839,"version":"3.50.1"},"reference-count":36,"publisher":"Elsevier BV","issue":"2","license":[{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T00:00:00Z","timestamp":1700524800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072469"],"award-info":[{"award-number":["62072469"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["High-Confidence Computing"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1016\/j.hcc.2023.100179","type":"journal-article","created":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T18:59:05Z","timestamp":1700765945000},"page":"100179","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":17,"title":["FedQMIX: Communication-efficient federated learning via multi-agent reinforcement learning"],"prefix":"10.1016","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8287-2942","authenticated-orcid":false,"given":"Shaohua","family":"Cao","sequence":"first","affiliation":[]},{"given":"Hanqing","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Tian","family":"Wen","sequence":"additional","affiliation":[]},{"given":"Hongwei","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Quancheng","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Weishan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Danyang","family":"Zheng","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.hcc.2023.100179_b1","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.comcom.2023.04.012","article-title":"FedMBC: Personalized federated learning via mutually beneficial collaboration","volume":"205","author":"Gong","year":"2023","journal-title":"Comput. Commun."},{"issue":"99","key":"10.1016\/j.hcc.2023.100179_b2","first-page":"1","article-title":"Mobile edge computing: A survey","volume":"PP","author":"Abbas","year":"2017","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.hcc.2023.100179_b3","doi-asserted-by":"crossref","unstructured":"Q. Li, Y. Diao, Q. Chen, B. He, Federated Learning on Non-IID Data Silos: An Experimental Study, in: 2022 IEEE 38th International Conference on Data Engineering, 2022, pp. 965\u2013978.","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"10.1016\/j.hcc.2023.100179_b4","series-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume":"vol. 54","author":"McMahan","year":"2017"},{"key":"10.1016\/j.hcc.2023.100179_b5","series-title":"International Conference on Machine Learning","first-page":"3973","article-title":"Fedboost: A communication-efficient algorithm for federated learning","author":"Hamer","year":"2020"},{"issue":"2","key":"10.1016\/j.hcc.2023.100179_b6","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.1109\/TNSE.2020.3014385","article-title":"Learning in the air: Secure federated learning for UAV-assisted crowdsensing","volume":"8","author":"Wang","year":"2021","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"issue":"2","key":"10.1016\/j.hcc.2023.100179_b7","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1109\/TNSE.2020.2996612","article-title":"FedSteg: A federated transfer learning framework for secure image steganalysis","volume":"8","author":"Yang","year":"2021","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"issue":"6","key":"10.1016\/j.hcc.2023.100179_b8","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/MIS.2021.3082561","article-title":"SecureBoost: A lossless federated learning framework","volume":"36","author":"Cheng","year":"2021","journal-title":"IEEE Intell. Syst."},{"issue":"2","key":"10.1016\/j.hcc.2023.100179_b9","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1109\/MIS.2022.3151466","article-title":"Robust federated learning with noisy labels","volume":"37","author":"Yang","year":"2022","journal-title":"IEEE Intell. Syst."},{"issue":"9","key":"10.1016\/j.hcc.2023.100179_b10","doi-asserted-by":"crossref","first-page":"3400","DOI":"10.1109\/TNNLS.2019.2944481","article-title":"Robust and communication-efficient federated learning from non-iid data","volume":"31","author":"Sattler","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.hcc.2023.100179_b11","series-title":"IEEE INFOCOM 2023-IEEE Conference on Computer Communications","first-page":"1","article-title":"Communication-efficient federated learning for heterogeneous edge devices based on adaptive gradient quantization","author":"Liu","year":"2023"},{"issue":"1","key":"10.1016\/j.hcc.2023.100179_b12","doi-asserted-by":"crossref","first-page":"2032","DOI":"10.1038\/s41467-022-29763-x","article-title":"Communication-efficient federated learning via knowledge distillation","volume":"13","author":"Wu","year":"2022","journal-title":"Nat. Commun."},{"key":"10.1016\/j.hcc.2023.100179_b13","series-title":"8th International Conference on Learning Representations","article-title":"On the convergence of FedAvg on non-IID data","author":"Li","year":"2020"},{"issue":"7","key":"10.1016\/j.hcc.2023.100179_b14","doi-asserted-by":"crossref","first-page":"1655","DOI":"10.1109\/TC.2021.3099723","article-title":"Adaptive federated learning on non-iid data with resource constraint","volume":"71","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Comput."},{"key":"10.1016\/j.hcc.2023.100179_b15","doi-asserted-by":"crossref","unstructured":"K. Wang, X. Ye, K. Sakurai, Federated Learning with Clustering-Based Participant Selection for IoT Applications, in: 2022 IEEE International Conference on Big Data, 2022, pp. 6830\u20136831.","DOI":"10.1109\/BigData55660.2022.10020575"},{"issue":"11","key":"10.1016\/j.hcc.2023.100179_b16","doi-asserted-by":"crossref","first-page":"8719","DOI":"10.1109\/TNNLS.2022.3152581","article-title":"Federated learning with taskonomy for non-IID data","volume":"34","author":"Jamali-Rad","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.hcc.2023.100179_b17","doi-asserted-by":"crossref","unstructured":"M. Tang, X. Ning, Y. Wang, J. Sun, Y. Wang, H.H. Li, Y. Chen, FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning, in: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 10092\u201310101.","DOI":"10.1109\/CVPR52688.2022.00986"},{"issue":"10","key":"10.1016\/j.hcc.2023.100179_b18","doi-asserted-by":"crossref","first-page":"7235","DOI":"10.1002\/int.22879","article-title":"Contribution-based federated learning client selection","volume":"37","author":"Lin","year":"2022","journal-title":"Int. J. Intell. Syst."},{"issue":"8","key":"10.1016\/j.hcc.2023.100179_b19","doi-asserted-by":"crossref","first-page":"8829","DOI":"10.1109\/TII.2022.3222369","article-title":"R 2 Fed: Resilient reinforcement federated learning for industrial applications","volume":"19","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"99","key":"10.1016\/j.hcc.2023.100179_b20","first-page":"1","article-title":"Deep reinforcement learning assisted federated learning algorithm for data management of IIoT","volume":"PP","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.hcc.2023.100179_b21","doi-asserted-by":"crossref","unstructured":"H. Wang, Z. Kaplan, D. Niu, B. Li, Optimizing Federated Learning on Non-IID Data with Reinforcement Learning, in: IEEE Conference on Computer Communications, 2020, pp. 1698\u20131707.","DOI":"10.1109\/INFOCOM41043.2020.9155494"},{"key":"10.1016\/j.hcc.2023.100179_b22","series-title":"Federated learning: Strategies for improving communication efficiency","author":"Konen","year":"2016"},{"key":"10.1016\/j.hcc.2023.100179_b23","doi-asserted-by":"crossref","unstructured":"N. Yoshida, T. Nishio, M. Morikura, K. Yamamoto, R. Yonetani, Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Data, in: 2020 IEEE International Conference on Communications, pp. 1\u20137.","DOI":"10.1109\/ICC40277.2020.9149323"},{"key":"10.1016\/j.hcc.2023.100179_b24","doi-asserted-by":"crossref","unstructured":"M. Duan, D. Liu, X. Chen, Y. Tan, J. Ren, L. Qiao, L. Liang, Astraea: Self-Balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applications, in: IEEE 37th International Conference on Computer Design, 2019, pp. 246\u2013254.","DOI":"10.1109\/ICCD46524.2019.00038"},{"key":"10.1016\/j.hcc.2023.100179_b25","series-title":"Federated learning with non-IID data","author":"Zhao","year":"2018"},{"key":"10.1016\/j.hcc.2023.100179_b26","series-title":"International Conference on Artificial Intelligence and Statistics, Vol. 151","first-page":"18","article-title":"Federated reinforcement learning with environment heterogeneity","author":"Jin","year":"2022"},{"key":"10.1016\/j.hcc.2023.100179_b27","series-title":"2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering","first-page":"123","article-title":"Federated reinforcement learning for fast personalization","author":"Nadiger","year":"2019"},{"issue":"10","key":"10.1016\/j.hcc.2023.100179_b28","doi-asserted-by":"crossref","first-page":"9441","DOI":"10.1109\/JIOT.2020.2986803","article-title":"Federated deep reinforcement learning for Internet of Things with decentralized cooperative edge caching","volume":"7","author":"Wang","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.hcc.2023.100179_b29","first-page":"1","article-title":"Non-IID federated learning with sharper risk bound","author":"Wei","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.hcc.2023.100179_b30","doi-asserted-by":"crossref","unstructured":"J.S. Nightingale, Y. Wang, F. Zobiri, M.A. Mustafa, Effect of Clustering in Federated Learning on Non-IID Electricity Consumption Prediction, in: 2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, 2022, pp. 1\u20135.","DOI":"10.1109\/ISGT-Europe54678.2022.9960569"},{"issue":"4","key":"10.1016\/j.hcc.2023.100179_b31","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1109\/MIS.2022.3168298","article-title":"Contribution- and participation-based federated learning on non-IID data","volume":"37","author":"Hu","year":"2022","journal-title":"IEEE Intell. Syst."},{"issue":"1","key":"10.1016\/j.hcc.2023.100179_b32","article-title":"Monotonic value function factorisation for deep multi-agent reinforcement learning","volume":"21","author":"Rashid","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.hcc.2023.100179_b33","series-title":"Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems","first-page":"2085","article-title":"Value-decomposition networks for cooperative multi-agent learning based on team reward","author":"Sunehag","year":"2018"},{"issue":"11","key":"10.1016\/j.hcc.2023.100179_b34","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"issue":"4","key":"10.1016\/j.hcc.2023.100179_b35","article-title":"Learning multiple layers of features from tiny images","volume":"1","author":"Krizhevsky","year":"2009","journal-title":"Handb. Syst. Autoimmu. Dis."},{"key":"10.1016\/j.hcc.2023.100179_b36","series-title":"International Conference on Machine Learning","first-page":"12878","article-title":"Data-free knowledge distillation for heterogeneous federated learning","author":"Zhu","year":"2021"}],"container-title":["High-Confidence Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2667295223000776?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2667295223000776?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T03:01:36Z","timestamp":1718593296000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2667295223000776"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6]]},"references-count":36,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["S2667295223000776"],"URL":"https:\/\/doi.org\/10.1016\/j.hcc.2023.100179","relation":{},"ISSN":["2667-2952"],"issn-type":[{"value":"2667-2952","type":"print"}],"subject":[],"published":{"date-parts":[[2024,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"FedQMIX: Communication-efficient federated learning via multi-agent reinforcement learning","name":"articletitle","label":"Article Title"},{"value":"High-Confidence Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.hcc.2023.100179","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 The Author(s). Published by Elsevier B.V. on behalf of Shandong University.","name":"copyright","label":"Copyright"}],"article-number":"100179"}}