{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T16:07:59Z","timestamp":1780762079645,"version":"3.54.1"},"reference-count":43,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Federated learning (FL) has emerged as a paradigm-shifting approach for collaborative machine learning (ML) while preserving data privacy. However, existing FL frameworks face significant challenges in ensuring trustworthiness, regulatory compliance, and security across heterogeneous institutional environments. We introduce AspectFL, a novel aspect-oriented programming (AOP) framework that seamlessly integrates trust, compliance, and security concerns into FL systems through cross-cutting aspect weaving. Our framework implements four core aspects: FAIR (Findability, Accessibility, Interoperability, Reusability) compliance, security threat detection and mitigation, provenance tracking, and institutional policy enforcement. AspectFL employs a sophisticated aspect weaver that intercepts FL execution at critical joinpoints, enabling dynamic policy enforcement and real-time compliance monitoring without modifying core learning algorithms. We demonstrate AspectFL\u2019s effectiveness through experiments on healthcare and financial datasets, including a detailed and reproducible evaluation on the real-world MIMIC-III dataset. Our results, reported with 95% confidence intervals and validated with appropriate statistical tests, show significant improvements in model performance, with a 4.52% and 0.90% increase in Area Under the Curve (AUC) for the healthcare and financial scenarios, respectively. Furthermore, we present a detailed ablation study, a comparative benchmark against existing FL frameworks, and an empirical scalability analysis, demonstrating the practical viability of our approach. AspectFL achieves high FAIR compliance scores (0.762), robust security (0.798 security score), and consistent policy adherence (over 84%), establishing a new standard for trustworthy FL.<\/jats:p>","DOI":"10.3390\/info16121048","type":"journal-article","created":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T13:03:02Z","timestamp":1764594182000},"page":"1048","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["AspectFL: Aspect-Oriented Programming for Trustworthy and Compliant Federated Learning Systems"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1506-7924","authenticated-orcid":false,"given":"Anas","family":"AlSobeh","sequence":"first","affiliation":[{"name":"Information Systems and Technology Department, Utah Valley University, Orem, UT 84058, USA"},{"name":"School of Computing, Southern Illinois University, Carbondale, IL 62901, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5096-9405","authenticated-orcid":false,"given":"Amani","family":"Shatnawi","sequence":"additional","affiliation":[{"name":"School of Computing, Weber State University, Ogden, UT 84408, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aws","family":"Magableh","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Prince Sultan University, Riyadh 11586, Saudi Arabia"},{"name":"Department of Computer Information Systems, Yarmouk University, Irbid 21163, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,1]]},"reference":[{"key":"ref_1","first-page":"50","article-title":"Federated learning: Challenges, methods, and future directions","volume":"37","author":"Li","year":"2020","journal-title":"IEEE Signal Process. 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