{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T06:02:48Z","timestamp":1771567368500,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T00:00:00Z","timestamp":1771372800000},"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":"crossref","award":["62273142"],"award-info":[{"award-number":["62273142"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["52272347"],"award-info":[{"award-number":["52272347"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Natural Science Foundation of China of Hunan Province","award":["2024JJ7132"],"award-info":[{"award-number":["2024JJ7132"]}]},{"name":"Hunan Provincial Key Laboratory for Control Technology of Distributed Electric Propulsion Aircraft"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Federated Learning (FL) enables collaborative model training without sharing raw data, offering a promising solution for privacy-sensitive applications. However, in real-world deployments, significant disparities in client computational capabilities lead to imbalanced model updates, resulting in slow convergence and degraded model generalization. To address this challenge, this paper proposes a novel federated aggregation optimization method, FedAWR, which features adaptive adjustment of learning rates and weights. Specifically, during the global aggregation phase, our method dynamically adjusts each client\u2019s aggregation weight based on its computational capability and configures an appropriate learning rate to balance training progress. Experiments on multi-classification tasks using the Steel Rail Defect and CIFAR-10 datasets demonstrate that the proposed method exhibits significant advantages over mainstream federated algorithms in both convergence efficiency and model generalization performance, thereby validating its effectiveness and superiority.<\/jats:p>","DOI":"10.3390\/fi18020106","type":"journal-article","created":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T11:45:58Z","timestamp":1771415158000},"page":"106","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FedAWR: Aggregation Optimization in Federated Learning with Adaptive Weights and Learning Rates"],"prefix":"10.3390","volume":"18","author":[{"given":"Tong","family":"Yao","sequence":"first","affiliation":[{"name":"School of Transportation and Electrical Engineering, Hunan University of Technology, Zhuzhou 412007, China"}]},{"given":"Jianqi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Hunan Institute of Science and Technology, Yueyang 414006, China"},{"name":"School of Computer and Electrical Engineering, Hunan University of Arts and Science, Changde 415000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1694-0975","authenticated-orcid":false,"given":"Jianhua","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Transportation and Electrical Engineering, Hunan University of Technology, Zhuzhou 412007, China"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,18]]},"reference":[{"key":"ref_1","unstructured":"Zhang, H., Hong, J., Dong, F., Drew, S., Xue, L., and Zhou, J. 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