{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T03:33:57Z","timestamp":1785382437042,"version":"3.55.0"},"reference-count":51,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T00:00:00Z","timestamp":1750809600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"JSPS Grant-in-Aid for Scientific Research","doi-asserted-by":"publisher","award":["23K11103"],"award-info":[{"award-number":["23K11103"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCP"],"abstract":"<jats:p>Federated learning enables privacy-preserving model training across distributed clients, yet real-world deployments face statistical, system, and behavioral heterogeneity, which degrades performance and increases vulnerability to adversarial clients. Existing incentive mechanisms often neglect participant credibility, leading to unfair rewards and reduced robustness. To address these issues, we propose a Trust-Aware Incentive Mechanism (TAIM), which evaluates client reliability through a multi-dimensional trust model incorporating participation frequency, gradient consistency, and contribution effectiveness. A trust-weighted reward allocation is formulated via a Stackelberg game, and a confidence-based soft filtering algorithm is introduced to mitigate the impact of unreliable updates. Experiments on FEMNIST, CIFAR-10, and Sent140 demonstrate that TAIM improves accuracy by up to 4.1%, reduces performance degradation under adaptive attacks by over 35%, and ensures fairer incentive distribution with a Gini coefficient below 0.3. TAIM offers a robust and equitable FL framework suitable for heterogeneous edge environments.<\/jats:p>","DOI":"10.3390\/jcp5030037","type":"journal-article","created":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T03:39:59Z","timestamp":1750909199000},"page":"37","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Trust-Aware Incentive Mechanism for Federated Learning with Heterogeneous Clients in Edge Computing"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-8854-7519","authenticated-orcid":false,"given":"Jiantao","family":"Xu","sequence":"first","affiliation":[{"name":"Graduate School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Fukushima Prefecture, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8878-0560","authenticated-orcid":false,"given":"Chen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Graduate School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Fukushima Prefecture, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2113-514X","authenticated-orcid":false,"given":"Liu","family":"Jin","sequence":"additional","affiliation":[{"name":"Graduate School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Fukushima Prefecture, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6461-9684","authenticated-orcid":false,"given":"Chunhua","family":"Su","sequence":"additional","affiliation":[{"name":"Graduate School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Fukushima Prefecture, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1016\/j.future.2024.07.035","article-title":"Privacy-preserving edge federated learning for intelligent mobile-health systems","volume":"161","author":"Aminifar","year":"2024","journal-title":"Future Gener. 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