{"id":12338,"date":"2026-09-25T15:51:17","date_gmt":"2026-09-25T07:51:17","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=12338"},"modified":"2026-09-25T20:18:43","modified_gmt":"2026-09-25T12:18:43","slug":"jase-202612-35-061","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202612-35-061","title":{"rendered":"Federated Learning for Data Security and Trust in University Sports Systems"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 35<\/a><\/p>\n<\/div>\n\n\n\n

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Yujia Wang<\/i><\/a><\/p>\n\n\n\n

Criminal Investigation Police University of China, Shenyang, 110854, China<\/p>\n<\/div>\n\n\n\n

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Received: August 24, 2026
Accepted: September 10, 2026
Publication Date: September 25, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Model Performance under Different Module-Ablation Settings<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation: BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202612_35.061<\/a> <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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University sports information systems continuously aggregate data from fitness tests, courses, venue use, check-ins, and wearable devices-multi-source, heterogeneous, distributed, and privacy-sensitive data whose centralized modeling exposes students’ sports and health information, complicates cross-node collaboration, and limits scalability. We therefore build a secure, trustworthy federated learning mechanism for university sports information systems with a six-layer framework spanning data acquisition through application services. After local training it applies gradient pruning, differential-privacy perturbation, and secure aggregation, while node trustworthiness-from contribution, update stability, anomaly risk, and historical reputation-drives trust-weighted aggregation, coupling privacy protection with dynamic credibility control rather than treating them as independent modules. Under non-IID distributions, simulations show the method achieves accuracy, recall, F1, and AUC of 0.887, 0.873, 0.879, and 0.914, exceeding FedAvg’s F1 by about 5.8 points and DP-FedAvg’s by about 6.7; with 30% malicious nodes it still holds F1=0.831 versus 0.702 for FedAvg. The mechanism thus improves performance, privacy, and anomalous-node suppression without raw sports data leaving local nodes.<\/p>\n\n\n\n

Keywords: data security; trustworthiness assessment; differential privacy; secure aggregation; non-IID data.<\/em><\/p>\n\n\n\n

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