{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T14:27:18Z","timestamp":1786631238593,"version":"build-2736575974"},"reference-count":51,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:00:00Z","timestamp":1783382400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Communications"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.comcom.2026.108618","type":"journal-article","created":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T06:46:04Z","timestamp":1783406764000},"page":"108618","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Shapley value-based fair asynchronous federated learning"],"prefix":"10.1016","volume":"257","author":[{"given":"Liyong","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangyong","family":"Shang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4136-4735","authenticated-orcid":false,"given":"Rui","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.comcom.2026.108618_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.comcom.2024.107964","article-title":"Federated learning: A cutting-edge survey of the latest advancements and applications","volume":"228","author":"Akhtarshenas","year":"2024","journal-title":"Comput. Commun."},{"key":"10.1016\/j.comcom.2026.108618_b2","doi-asserted-by":"crossref","first-page":"S116","DOI":"10.1111\/epi.16555","article-title":"Machine learning and wearable devices of the future","volume":"62","author":"Beniczky","year":"2021","journal-title":"Epilepsia"},{"key":"10.1016\/j.comcom.2026.108618_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.comcom.2025.108055","article-title":"AI-based malware detection in IoT networks within smart cities: A survey","author":"Alhamdi","year":"2025","journal-title":"Comput. Commun."},{"key":"10.1016\/j.comcom.2026.108618_b4","first-page":"2016","article-title":"Regulation (EU) 2016\/679 of the European parliament and of the council","volume":"679","author":"Regulation","year":"2016","journal-title":"Regul. (Eu)"},{"key":"10.1016\/j.comcom.2026.108618_b5","series-title":"Artificial Intelligence and Statistics","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017"},{"key":"10.1016\/j.comcom.2026.108618_b6","series-title":"Proceedings of the 25th International Conference on Artificial Intelligence and Statistics","first-page":"3581","article-title":"Federated learning with buffered asynchronous aggregation","volume":"vol. 151","author":"Nguyen","year":"2022"},{"key":"10.1016\/j.comcom.2026.108618_b7","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.comcom.2024.06.003","article-title":"VSFL: Trajectory prediction framework based on validity-aware semi-asynchronous federated learning in internet of vehicles","volume":"224","author":"Li","year":"2024","journal-title":"Comput. Commun."},{"key":"10.1016\/j.comcom.2026.108618_b8","series-title":"Asynchronous federated optimization","author":"Xie","year":"2019"},{"issue":"12","key":"10.1016\/j.comcom.2026.108618_b9","doi-asserted-by":"crossref","first-page":"3291","DOI":"10.1109\/TPDS.2022.3150579","article-title":"Towards efficient and stable K-asynchronous federated learning with unbounded stale gradients on non-IID data","volume":"33","author":"Zhou","year":"2022","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"10.1016\/j.comcom.2026.108618_b10","first-page":"13900","article-title":"Fedasmu: Efficient asynchronous federated learning with dynamic staleness-aware model update","volume":"vol. 38","author":"Liu","year":"2024"},{"issue":"2","key":"10.1016\/j.comcom.2026.108618_b11","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/TMC.2021.3096846","article-title":"Adaptive asynchronous federated learning in resource-constrained edge computing","volume":"22","author":"Liu","year":"2023","journal-title":"IEEE Trans. Mob. Comput."},{"issue":"01","key":"10.1016\/j.comcom.2026.108618_b12","first-page":"1","article-title":"Staleness-controlled asynchronous federated learning: Accuracy and efficiency tradeoff","author":"Sun","year":"2024","journal-title":"IEEE Trans. Mob. Comput."},{"key":"10.1016\/j.comcom.2026.108618_b13","doi-asserted-by":"crossref","DOI":"10.1016\/j.jpdc.2024.104950","article-title":"Staleness aware semi-asynchronous federated learning","volume":"193","author":"Yu","year":"2024","journal-title":"J. Parallel Distrib. Comput."},{"issue":"5","key":"10.1016\/j.comcom.2026.108618_b14","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1109\/TC.2020.2994391","article-title":"SAFA: A semi-asynchronous protocol for fast federated learning with low overhead","volume":"70","author":"Wu","year":"2020","journal-title":"IEEE Trans. Comput."},{"key":"10.1016\/j.comcom.2026.108618_b15","series-title":"Learning from straggler clients in federated learning","author":"Hard","year":"2024"},{"issue":"8","key":"10.1016\/j.comcom.2026.108618_b16","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","article-title":"Representation learning: A review and new perspectives","volume":"35","author":"Bengio","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"4","key":"10.1016\/j.comcom.2026.108618_b17","doi-asserted-by":"crossref","first-page":"2493","DOI":"10.1109\/TWC.2022.3211998","article-title":"Online client selection for asynchronous federated learning with fairness consideration","volume":"22","author":"Zhu","year":"2022","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"10.1016\/j.comcom.2026.108618_b18","series-title":"2024 IEEE 44th International Conference on Distributed Computing Systems","first-page":"797","article-title":"Libra: A fairness-guaranteed framework for semi-asynchronous federated learning","author":"Wang","year":"2024"},{"issue":"1","key":"10.1016\/j.comcom.2026.108618_b19","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1186\/s13677-023-00535-2","article-title":"FedEem: a fairness-based asynchronous federated learning mechanism","volume":"12","author":"Gu","year":"2023","journal-title":"J. Cloud Comput."},{"key":"10.1016\/j.comcom.2026.108618_b20","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.comcom.2022.09.016","article-title":"AFAFed\u2014Asynchronous fair adaptive federated learning for IoT stream applications","volume":"195","author":"Baccarelli","year":"2022","journal-title":"Comput. Commun."},{"key":"10.1016\/j.comcom.2026.108618_b21","series-title":"2022 IEEE 38th International Conference on Data Engineering","first-page":"911","article-title":"Efficient participant contribution evaluation for horizontal and vertical federated learning","author":"Wang","year":"2022"},{"key":"10.1016\/j.comcom.2026.108618_b22","doi-asserted-by":"crossref","unstructured":"Q. Sun, X. Li, J. Zhang, L. Xiong, W. Liu, J. Liu, Z. Qin, K. Ren, Shapleyfl: Robust federated learning based on shapley value, in: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023, pp. 2096\u20132108.","DOI":"10.1145\/3580305.3599500"},{"key":"10.1016\/j.comcom.2026.108618_b23","first-page":"9046","article-title":"Game of gradients: Mitigating irrelevant clients in federated learning","volume":"vol. 35","author":"Nagalapatti","year":"2021"},{"key":"10.1016\/j.comcom.2026.108618_b24","series-title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","first-page":"2350","article-title":"Staleness-aware async-SGD for distributed deep learning","author":"Zhang","year":"2016"},{"key":"10.1016\/j.comcom.2026.108618_b25","series-title":"2022 58th Annual Allerton Conference on Communication, Control, and Computing","first-page":"1","article-title":"Unbounded gradients in federated learning with buffered asynchronous aggregation","author":"Toghani","year":"2022"},{"key":"10.1016\/j.comcom.2026.108618_b26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TPAMI.2023.3347082","article-title":"Federated learning for generalization, robustness, fairness: A survey and benchmark","author":"Huang","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.comcom.2026.108618_b27","first-page":"2456","article-title":"Fairness in federated learning: Fairness for whom?","volume":"vol. 8","author":"Taik","year":"2025"},{"key":"10.1016\/j.comcom.2026.108618_b28","series-title":"Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency","article-title":"Benefits of the federation? Analyzing the impact of fair federated learning at the client level","author":"Corbucci","year":"2025"},{"key":"10.1016\/j.comcom.2026.108618_b29","series-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","first-page":"4100","article-title":"Towards fair federated learning","author":"Zhou","year":"2021"},{"key":"10.1016\/j.comcom.2026.108618_b30","series-title":"International Conference on Machine Learning","first-page":"4615","article-title":"Agnostic federated learning","author":"Mohri","year":"2019"},{"key":"10.1016\/j.comcom.2026.108618_b31","unstructured":"T. Li, M. Sanjabi, A. Beirami, V. Smith, Fair Resource Allocation in Federated Learning, in: International Conference on Learning Representations, 2020."},{"key":"10.1016\/j.comcom.2026.108618_b32","series-title":"Fairness and accuracy in federated learning","author":"Huang","year":"2020"},{"key":"10.1016\/j.comcom.2026.108618_b33","doi-asserted-by":"crossref","unstructured":"Z. Wang, X. Fan, J. Qi, C. Wen, C. Wang, R. Yu, Federated Learning with Fair Averaging, in: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, 2021, pp. 1615\u20131623.","DOI":"10.24963\/ijcai.2021\/223"},{"issue":"2","key":"10.1016\/j.comcom.2026.108618_b34","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1109\/TPAMI.2023.3332428","article-title":"Multi-stage asynchronous federated learning with adaptive differential privacy","volume":"46","author":"Li","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.comcom.2026.108618_b35","article-title":"A value for n-person games","volume":"2","author":"Shapley","year":"1953","journal-title":"Contrib. To Theory Games"},{"key":"10.1016\/j.comcom.2026.108618_b36","doi-asserted-by":"crossref","unstructured":"R.T. Ma, D.M. Chiu, J.C. Lui, V. Misra, D. Rubenstein, Internet Economics: The use of Shapley value for ISP settlement, in: Proceedings of the 2007 ACM CoNEXT Conference, 2007, pp. 1\u201312.","DOI":"10.1145\/1364654.1364662"},{"issue":"8","key":"10.1016\/j.comcom.2026.108618_b37","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1287\/mnsc.28.8.869","article-title":"The shapley value as aircraft landing fees\u2013revisited","volume":"28","author":"Dubey","year":"1982","journal-title":"Manag. Sci."},{"key":"10.1016\/j.comcom.2026.108618_b38","series-title":"International Conference on Artificial Intelligence and Statistics","first-page":"8780","article-title":"Beta Shapley: a unified and noise-reduced data valuation framework for machine learning","author":"Kwon","year":"2022"},{"key":"10.1016\/j.comcom.2026.108618_b39","first-page":"16104","article-title":"Gradient driven rewards to guarantee fairness in collaborative machine learning","volume":"34","author":"Xu","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.comcom.2026.108618_b40","series-title":"2022 IEEE\/ACM 30th International Symposium on Quality of Service","first-page":"1","article-title":"How asynchronous can federated learning be?","author":"Su","year":"2022"},{"key":"10.1016\/j.comcom.2026.108618_b41","series-title":"FedStaleWeight: Buffered asynchronous federated learning with fair aggregation via staleness reweighting","author":"Ma","year":"2024"},{"key":"10.1016\/j.comcom.2026.108618_b42","doi-asserted-by":"crossref","first-page":"30542","DOI":"10.52202\/068431-2214","article-title":"Trade-off between payoff and model rewards in Shapley-fair collaborative machine learning","volume":"35","author":"Nguyen","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.comcom.2026.108618_b43","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/978-3-030-63076-8_11","article-title":"A principled approach to data valuation for federated learning","author":"Wang","year":"2020","journal-title":"Fed. Learn.: Priv. Incent."},{"key":"10.1016\/j.comcom.2026.108618_b44","series-title":"7th International Conference on Learning Representations, ICLR 2019, New Orleans, la, USA, May 6-9, 2019","article-title":"Toward understanding the impact of staleness in distributed machine learning","author":"Dai","year":"2019"},{"issue":"5","key":"10.1016\/j.comcom.2026.108618_b45","doi-asserted-by":"crossref","first-page":"1726","DOI":"10.1016\/j.cor.2008.04.004","article-title":"Polynomial calculation of the Shapley value based on sampling","volume":"36","author":"Castro","year":"2009","journal-title":"Comput. Oper. Res."},{"key":"10.1016\/j.comcom.2026.108618_b46","unstructured":"X. Xu, L. Lyu, A Reputation Mechanism Is All You Need: Collaborative Fairness and Adversarial Robustness in Federated Learning, in: International Workshop on Federated Learning for User Privacy and Data Confidentiality in Conjunction with ICML 2021, FL-ICML\u201921, 2021."},{"key":"10.1016\/j.comcom.2026.108618_b47","series-title":"2016 54th Annual Allerton Conference on Communication, Control, and Computing","first-page":"997","article-title":"Asynchrony begets momentum, with an application to deep learning","author":"Mitliagkas","year":"2016"},{"key":"10.1016\/j.comcom.2026.108618_b48","series-title":"Measuring the effects of non-identical data distribution for federated visual classification","author":"Hsu","year":"2019"},{"key":"10.1016\/j.comcom.2026.108618_b49","series-title":"2024 IEEE\/ACM 32nd International Symposium on Quality of Service","first-page":"1","article-title":"Leave no one behind: Unleashing stragglers\u2019 potential for accurate and realtime asynchronous federated learning","author":"Chen","year":"2024"},{"issue":"100","key":"10.1016\/j.comcom.2026.108618_b50","first-page":"1","article-title":"FedLab: A flexible federated learning framework","volume":"24","author":"Zeng","year":"2023","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.comcom.2026.108618_b51","series-title":"Leaf: A benchmark for federated settings","author":"Caldas","year":"2018"}],"container-title":["Computer Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0140366426002082?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0140366426002082?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T13:29:00Z","timestamp":1786627740000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0140366426002082"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":51,"alternative-id":["S0140366426002082"],"URL":"https:\/\/doi.org\/10.1016\/j.comcom.2026.108618","relation":{},"ISSN":["0140-3664"],"issn-type":[{"value":"0140-3664","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Shapley value-based fair asynchronous federated learning","name":"articletitle","label":"Article Title"},{"value":"Computer Communications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.comcom.2026.108618","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"108618"}}