{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T00:29:55Z","timestamp":1775521795118,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,18]],"date-time":"2025-04-18T00:00:00Z","timestamp":1744934400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Different from conventional federated learning (FL), which relies on a central server for model aggregation, decentralized FL (DFL) exchanges models among edge servers, thus improving the robustness and scalability. When deploying DFL into the Internet of Things (IoT), limited wireless resources cannot provide simultaneous access to massive devices. One must perform client scheduling to balance the convergence rate and model accuracy. However, the heterogeneity of computing and communication resources across client devices, combined with the time-varying nature of wireless channels, makes it challenging to estimate accurately the delay associated with client participation during the scheduling process. To address this issue, we investigate the client scheduling and resource optimization problem in DFL without prior client information. Specifically, the considered problem is reformulated as a multi-armed bandit (MAB) program, and an online learning algorithm that utilizes contextual multi-arm slot machines for client delay estimation and scheduling is proposed. Through theoretical analysis, this algorithm can achieve asymptotic optimal performance in theory. The experimental results show that the algorithm can make asymptotic optimal client selection decisions, and this method is superior to existing algorithms in reducing the cumulative delay of the system.<\/jats:p>","DOI":"10.3390\/e27040439","type":"journal-article","created":{"date-parts":[[2025,4,20]],"date-time":"2025-04-20T20:31:36Z","timestamp":1745181096000},"page":"439","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["MAB-Based Online Client Scheduling for Decentralized Federated Learning in the IoT"],"prefix":"10.3390","volume":"27","author":[{"given":"Zhenning","family":"Chen","sequence":"first","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siyang","family":"Wang","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing 210049, China"},{"name":"Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks, Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing 210049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youren","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1109\/JIOT.2021.3103320","article-title":"6G Internet of Things: A Comprehensive Survey","volume":"9","author":"Nguyen","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_2","first-page":"109","article-title":"Internet of things (IoT) in industry: Contemporary application domains, innovative technologies and intelligent manufacturing","volume":"6","author":"Lampropoulos","year":"2018","journal-title":"People"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: Trends, perspectives, and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"ref_4","unstructured":"Majeed, I.A., Kaushik, S., Bardhan, A., Tadi, V.S.K., Min, H.K., Kumaraguru, K., and Muni, R.D. 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