{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T22:21:00Z","timestamp":1783635660276,"version":"3.55.0"},"reference-count":56,"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":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62350710797"],"award-info":[{"award-number":["62350710797"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972114"],"award-info":[{"award-number":["61972114"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62106061"],"award-info":[{"award-number":["62106061"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017366","name":"Key Research and Development Program of Heilongjiang","doi-asserted-by":"publisher","award":["JD2023GJ01"],"award-info":[{"award-number":["JD2023GJ01"]}],"id":[{"id":"10.13039\/100017366","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017366","name":"Key Research and Development Program of Heilongjiang","doi-asserted-by":"publisher","award":["2021ZXJ05A03"],"award-info":[{"award-number":["2021ZXJ05A03"]}],"id":[{"id":"10.13039\/100017366","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017366","name":"Key Research and Development Program of Heilongjiang","doi-asserted-by":"publisher","award":["2022ZX01A22"],"award-info":[{"award-number":["2022ZX01A22"]}],"id":[{"id":"10.13039\/100017366","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2026YFE0201500"],"award-info":[{"award-number":["2026YFE0201500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013076","name":"National Major Science and Technology Projects of China","doi-asserted-by":"publisher","award":["2021ZD0110901"],"award-info":[{"award-number":["2021ZD0110901"]}],"id":[{"id":"10.13039\/501100013076","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011798","name":"Ministry of Agriculture and Rural Affairs of the People's Republic of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011798","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.knosys.2026.116328","type":"journal-article","created":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T15:05:00Z","timestamp":1779980700000},"page":"116328","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A Lyapunov-based client selection approach to handle system-induced heterogeneity in federated learning"],"prefix":"10.1016","volume":"348","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-5814-2312","authenticated-orcid":false,"given":"Tian","family":"Ren","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6769-2115","authenticated-orcid":false,"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weilin","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1263-9907","authenticated-orcid":false,"given":"Siyao","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.knosys.2026.116328_b1","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1109\/TBDATA.2024.3362191","article-title":"Decentralized federated learning: A survey on security and privacy","volume":"10","author":"Hallaji","year":"2024","journal-title":"IEEE Trans. Big Data"},{"issue":"4","key":"10.1016\/j.knosys.2026.116328_b2","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1109\/TCE.2023.3318150","article-title":"A comprehensive survey on artificial intelligence empowered edge computing on consumer electronics","volume":"69","author":"Syu","year":"2023","journal-title":"IEEE Trans. Consum. Electron."},{"issue":"6","key":"10.1016\/j.knosys.2026.116328_b3","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1109\/TMC.2019.2908403","article-title":"Joint communication, computation, caching, and control in big data multi-access edge computing","volume":"19","author":"Ndikumana","year":"2020","journal-title":"IEEE Trans. Mob. Comput."},{"issue":"11","key":"10.1016\/j.knosys.2026.116328_b4","doi-asserted-by":"crossref","first-page":"20514","DOI":"10.1109\/JIOT.2024.3372016","article-title":"A Stackelberg-game-based framework for edge pricing and resource allocation in mobile edge computing","volume":"11","author":"Cheng","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.knosys.2026.116328_b5","series-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, 20-22 April 2017, Fort Lauderdale, FL, USA","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume":"vol. 54","author":"McMahan","year":"2017"},{"issue":"4","key":"10.1016\/j.knosys.2026.116328_b6","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1109\/TCE.2023.3242375","article-title":"Consumer-centric internet of medical things for cyborg applications based on federated reinforcement learning","volume":"69","author":"Tiwari","year":"2023","journal-title":"IEEE Trans. Consum. Electron."},{"key":"10.1016\/j.knosys.2026.116328_b7","unstructured":"X. Li, K. Huang, W. Yang, S. Wang, Z. Zhang, On the Convergence of FedAvg on Non-IID Data, in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, 2020."},{"key":"10.1016\/j.knosys.2026.116328_b8","series-title":"Straggler-resilient federated learning: Leveraging the interplay between statistical accuracy and system heterogeneity","author":"Reisizadeh","year":"2020"},{"key":"10.1016\/j.knosys.2026.116328_b9","series-title":"43rd IEEE International Conference on Distributed Computing Systems, ICDCS 2023, Hong Kong, July 18-21, 2023","first-page":"1037","article-title":"Scalable federated learning with system heterogeneity","author":"Ilhan","year":"2023"},{"key":"10.1016\/j.knosys.2026.116328_b10","series-title":"38th IEEE International Conference on Data Engineering, ICDE 2022, Kuala Lumpur, Malaysia, May 9-12, 2022","first-page":"2575","article-title":"FedADMM: A robust federated deep learning framework with adaptivity to system heterogeneity","author":"Gong","year":"2022"},{"key":"10.1016\/j.knosys.2026.116328_b11","series-title":"RecSys \u201922: Sixteenth ACM Conference on Recommender Systems, Seattle, WA, USA, September 18 - 23, 2022","first-page":"156","article-title":"Towards fair federated recommendation learning: Characterizing the inter-dependence of system and data heterogeneity","author":"Maeng","year":"2022"},{"issue":"2","key":"10.1016\/j.knosys.2026.116328_b12","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1109\/JSAIT.2022.3205475","article-title":"Straggler-resilient federated learning: Leveraging the interplay between statistical accuracy and system heterogeneity","volume":"3","author":"Reisizadeh","year":"2022","journal-title":"IEEE J. Sel. Areas Inf. Theory"},{"key":"10.1016\/j.knosys.2026.116328_b13","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 2021, Virtual Event \/ Montreal, Canada, 19-27 August 2021, 2021, pp. 1615\u20131623.","DOI":"10.24963\/ijcai.2021\/223"},{"key":"10.1016\/j.knosys.2026.116328_b14","series-title":"IEEE INFOCOM 2022 - IEEE Conference on Computer Communications, London, United Kingdom, May 2-5, 2022","first-page":"1739","article-title":"Tackling system and statistical heterogeneity for federated learning with adaptive client sampling","author":"Luo","year":"2022"},{"key":"10.1016\/j.knosys.2026.116328_b15","unstructured":"J. Chen, H. Tang, J. Cheng, M. Yan, J. Zhang, M. Xu, Y. Hu, L. Nie, Breaking Barriers of System Heterogeneity: Straggler-Tolerant Multimodal Federated Learning via Knowledge Distillation, in: Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI 2024, Jeju, South Korea, August 3-9, 2024, 2024, pp. 3789\u20133797."},{"key":"10.1016\/j.knosys.2026.116328_b16","unstructured":"J. Liu, Y. Zhou, D. Wu, M. Hu, M. Guizani, Q.Z. Sheng, FedLMT: Tackling System Heterogeneity of Federated Learning via Low-Rank Model Training with Theoretical Guarantees, in: Forty-First International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024, 2024."},{"key":"10.1016\/j.knosys.2026.116328_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.113574","article-title":"Enhanced dynamic deep Q-network for federated learning scheduling policies on IoT devices using explanation-driven trust","volume":"318","author":"Rjoub","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2026.116328_b18","series-title":"Expanding the reach of federated learning by reducing client resource requirements","author":"Caldas","year":"2018"},{"key":"10.1016\/j.knosys.2026.116328_b19","series-title":"The 23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020, 26-28 August 2020, Online [Palermo, Sicily, Italy]","first-page":"2021","article-title":"FedPAQ: A communication-efficient federated learning method with periodic averaging and quantization","volume":"vol. 108","author":"Reisizadeh","year":"2020"},{"key":"10.1016\/j.knosys.2026.116328_b20","unstructured":"T. Li, A.K. Sahu, M. Zaheer, M. Sanjabi, A. Talwalkar, V. Smith, Federated Optimization in Heterogeneous Networks, in: Proceedings of the Third Conference on Machine Learning and Systems, MLSys 2020, Austin, TX, USA, March 2-4, 2020, 2020, pp. 429\u2013450."},{"key":"10.1016\/j.knosys.2026.116328_b21","series-title":"2021 IEEE Conference on Computer Communications Workshops, INFOCOM Workshops 2021, Vancouver, BC, Canada, May 10-13, 2021","first-page":"1","article-title":"Adaptive federated dropout: Improving communication efficiency and generalization for federated learning","author":"Bouacida","year":"2021"},{"key":"10.1016\/j.knosys.2026.116328_b22","series-title":"Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, February 22 - March 1, 2022","first-page":"8485","article-title":"SplitFed: When federated learning meets split learning","author":"Thapa","year":"2022"},{"issue":"17","key":"10.1016\/j.knosys.2026.116328_b23","doi-asserted-by":"crossref","first-page":"28798","DOI":"10.1109\/JIOT.2024.3406634","article-title":"An efficient asynchronous federated learning protocol for edge devices","volume":"11","author":"Li","year":"2024","journal-title":"IEEE Internet Things J."},{"issue":"10","key":"10.1016\/j.knosys.2026.116328_b24","doi-asserted-by":"crossref","first-page":"4353","DOI":"10.1109\/TPDS.2022.3186960","article-title":"Context-aware online client selection for hierarchical federated learning","volume":"33","author":"Qu","year":"2022","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"issue":"3","key":"10.1016\/j.knosys.2026.116328_b25","doi-asserted-by":"crossref","first-page":"3241","DOI":"10.1109\/TVT.2022.3144099","article-title":"The role of communication time in the convergence of federated edge learning","volume":"71","author":"Zhou","year":"2022","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"1","key":"10.1016\/j.knosys.2026.116328_b26","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1109\/TWC.2020.3024629","article-title":"A joint learning and communications framework for federated learning over wireless networks","volume":"20","author":"Chen","year":"2021","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"10.1016\/j.knosys.2026.116328_b27","series-title":"2019 IEEE International Conference on Communications, ICC 2019, Shanghai, China, May 20-24, 2019","first-page":"1","article-title":"Client selection for federated learning with heterogeneous resources in mobile edge","author":"Nishio","year":"2019"},{"issue":"9","key":"10.1016\/j.knosys.2026.116328_b28","doi-asserted-by":"crossref","first-page":"5962","DOI":"10.1109\/TCOMM.2021.3088528","article-title":"Joint client scheduling and resource allocation under channel uncertainty in federated learning","volume":"69","author":"Wadu","year":"2021","journal-title":"IEEE Trans. Commun."},{"key":"10.1016\/j.knosys.2026.116328_b29","unstructured":"S. Horv\u00e1th, S. Laskaridis, M. Almeida, I. Leontiadis, S.I. Venieris, N.D. Lane, FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout, in: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, Virtual, 2021, pp. 12876\u201312889."},{"issue":"5","key":"10.1016\/j.knosys.2026.116328_b30","doi-asserted-by":"crossref","first-page":"5462","DOI":"10.1109\/TMC.2023.3309633","article-title":"RingSFL: An adaptive split federated learning towards taming client heterogeneity","volume":"23","author":"Shen","year":"2024","journal-title":"IEEE Trans. Mob. Comput."},{"key":"10.1016\/j.knosys.2026.116328_b31","series-title":"2020 IEEE International Conference on Big Data (IEEE BigData 2020), Atlanta, GA, USA, December 10-13, 2020","first-page":"15","article-title":"Asynchronous online federated learning for edge devices with non-IID data","author":"Chen","year":"2020"},{"issue":"10","key":"10.1016\/j.knosys.2026.116328_b32","doi-asserted-by":"crossref","first-page":"4229","DOI":"10.1109\/TNNLS.2019.2953131","article-title":"Communication-efficient federated deep learning with layerwise asynchronous model update and temporally weighted aggregation","volume":"31","author":"Chen","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.knosys.2026.116328_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2025.111430","article-title":"AsyncDefender: Dynamic trust adaptation and collaborative defense for Byzantine-robust asynchronous federated learning","volume":"269","author":"Bai","year":"2025","journal-title":"Comput. Netw."},{"issue":"1","key":"10.1016\/j.knosys.2026.116328_b34","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1109\/TCOMM.2019.2944169","article-title":"Scheduling policies for federated learning in wireless networks","volume":"68","author":"Yang","year":"2020","journal-title":"IEEE Trans. Commun."},{"key":"10.1016\/j.knosys.2026.116328_b35","doi-asserted-by":"crossref","first-page":"23920","DOI":"10.1109\/ACCESS.2020.2968399","article-title":"Federated learning in vehicular edge computing: A selective model aggregation approach","volume":"8","author":"Ye","year":"2020","journal-title":"IEEE Access"},{"issue":"4","key":"10.1016\/j.knosys.2026.116328_b36","doi-asserted-by":"crossref","first-page":"7334","DOI":"10.1109\/TCE.2024.3397863","article-title":"Lyapunov-guided long-term fairness-aware federated learning for collaborative TinyML on edge devices","volume":"70","author":"Lu","year":"2024","journal-title":"IEEE Trans. Consum. Electron."},{"key":"10.1016\/j.knosys.2026.116328_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2024.110517","article-title":"Communication cost-aware client selection in online federated learning: A Lyapunov approach","volume":"249","author":"Su","year":"2024","journal-title":"Comput. Netw."},{"key":"10.1016\/j.knosys.2026.116328_b38","series-title":"Communication-efficient device scheduling for federated learning using Lyapunov optimization","author":"Perazzone","year":"2025"},{"issue":"2","key":"10.1016\/j.knosys.2026.116328_b39","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.1109\/TCOMM.2024.3443731","article-title":"Energy-efficient federated edge learning with streaming data: A Lyapunov optimization approach","volume":"73","author":"Hu","year":"2025","journal-title":"IEEE Trans. Commun."},{"issue":"2","key":"10.1016\/j.knosys.2026.116328_b40","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1109\/TNSE.2024.3508594","article-title":"Fairness-aware multi-server federated learning task delegation over wireless networks","volume":"12","author":"Gao","year":"2025","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"10.1016\/j.knosys.2026.116328_b41","series-title":"IEEE International Conference on Multimedia and Expo, ICME 2023, Brisbane, Australia, July 10-14, 2023","first-page":"324","article-title":"Fairness-aware client selection for federated learning","author":"Shi","year":"2023"},{"issue":"7","key":"10.1016\/j.knosys.2026.116328_b42","doi-asserted-by":"crossref","first-page":"1552","DOI":"10.1109\/TPDS.2020.3040887","article-title":"An efficiency-boosting client selection scheme for federated learning with fairness guarantee","volume":"32","author":"Huang","year":"2021","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"issue":"11","key":"10.1016\/j.knosys.2026.116328_b43","doi-asserted-by":"crossref","first-page":"7108","DOI":"10.1109\/TWC.2020.3008091","article-title":"Multi-armed bandit-based client scheduling for federated learning","volume":"19","author":"Xia","year":"2020","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"10.1016\/j.knosys.2026.116328_b44","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2025.111302","article-title":"Dynamic clustered federated learning via adaptive distribution similarity computation","volume":"265","author":"Ren","year":"2025","journal-title":"Comput. Netw."},{"key":"10.1016\/j.knosys.2026.116328_b45","series-title":"41st IEEE International Conference on Distributed Computing Systems, ICDCS 2021, Washington DC, USA, July 7-10, 2021","first-page":"35","article-title":"Incentive-driven long-term optimization for edge learning by hierarchical reinforcement mechanism","author":"Liu","year":"2021"},{"key":"10.1016\/j.knosys.2026.116328_b46","unstructured":"A. Josang, R. Ismail, The beta reputation system, in: Proceedings of the 15th Bled Electronic Commerce Conference, Vol. 5, 2002, pp. 2502\u20132511."},{"key":"10.1016\/j.knosys.2026.116328_b47","series-title":"38th IEEE International Conference on Data Engineering, ICDE 2022, Kuala Lumpur, Malaysia, May 9-12, 2022","first-page":"2440","article-title":"Improving fairness for data valuation in horizontal federated learning","author":"Fan","year":"2022"},{"issue":"4","key":"10.1016\/j.knosys.2026.116328_b48","doi-asserted-by":"crossref","first-page":"60:1","DOI":"10.1145\/3501811","article-title":"GTG-Shapley: Efficient and accurate participant contribution evaluation in federated learning","volume":"13","author":"Liu","year":"2022","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"10.1016\/j.knosys.2026.116328_b49","series-title":"2019 IEEE International Conference on Big Data (IEEE BigData), Los Angeles, CA, USA, December 9-12, 2019","first-page":"2577","article-title":"Profit allocation for federated learning","author":"Song","year":"2019"},{"issue":"12","key":"10.1016\/j.knosys.2026.116328_b50","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1016\/j.imavis.2004.03.008","article-title":"Improved method of handwritten digit recognition tested on MNIST database","volume":"22","author":"Kussul","year":"2004","journal-title":"Image Vis. Comput."},{"key":"10.1016\/j.knosys.2026.116328_b51","series-title":"Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017"},{"key":"10.1016\/j.knosys.2026.116328_b52","series-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"10.1016\/j.knosys.2026.116328_b53","series-title":"Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume":"vol. 119","author":"Karimireddy","year":"2020"},{"key":"10.1016\/j.knosys.2026.116328_b54","series-title":"41st IEEE International Conference on Computer Design, ICCD 2023, Washington, DC, USA, November 6-8, 2023","first-page":"444","article-title":"FLASH-RL: federated learning addressing system and static heterogeneity using reinforcement learning","author":"Bouaziz","year":"2023"},{"key":"10.1016\/j.knosys.2026.116328_b55","series-title":"International Conference on Artificial Intelligence and Statistics, AISTATS 2022, 28-30 March 2022, Virtual Event","first-page":"10351","article-title":"Towards understanding biased client selection in federated learning","volume":"vol. 151","author":"Cho","year":"2022"},{"key":"10.1016\/j.knosys.2026.116328_b56","series-title":"Leaf: A benchmark for federated settings","author":"Caldas","year":"2018"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126010543?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126010543?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T22:04:38Z","timestamp":1783634678000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126010543"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":56,"alternative-id":["S0950705126010543"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116328","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A Lyapunov-based client selection approach to handle system-induced heterogeneity in federated learning","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.116328","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"116328"}}