{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T16:59:48Z","timestamp":1783184388043,"version":"3.54.6"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685489","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:p>Decentralized Federated Learning (DFL) emerges as an innovative paradigm to train collaborative models, addressing the single point of failure limitation. However, the security and trustworthiness of FL and DFL are compromised by poisoning attacks, negatively impacting its performance. Existing defense mechanisms have been designed for centralized FL and they do not adequately exploit the particularities of DFL. Thus, this work introduces Sentinel, a defense strategy to counteract poisoning attacks in DFL. Sentinel leverages the accessibility of local data and defines a three-step aggregation protocol consisting of similarity filtering, bootstrap validation, and normalization to safeguard against malicious model updates. Sentinel has been evaluated with diverse datasets and data distributions. Besides, various poisoning attack types and threat levels have been verified. The results improve the state-of-the-art performance against both untargeted and targeted poisoning attacks when data follows an IID (Independent and Identically Distributed) configuration. Besides, under non-IID configuration, it is analyzed how performance degrades both for Sentinel and other state-of-the-art robust aggregation methods.<\/jats:p>","DOI":"10.3233\/faia240686","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:06:57Z","timestamp":1729170417000},"source":"Crossref","is-referenced-by-count":4,"title":["Sentinel: An Aggregation Function to Secure Decentralized Federated Learning"],"prefix":"10.3233","author":[{"given":"Chao","family":"Feng","sequence":"first","affiliation":[{"name":"Communication Systems Group CSG, Department of Informatics, University of Zurich UZH, CH\u20138050 Z\u00fcrich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alberto","family":"Huertas Celdr\u00e1n","sequence":"additional","affiliation":[{"name":"Communication Systems Group CSG, Department of Informatics, University of Zurich UZH, CH\u20138050 Z\u00fcrich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Janosch","family":"Baltensperger","sequence":"additional","affiliation":[{"name":"Communication Systems Group CSG, Department of Informatics, University of Zurich UZH, CH\u20138050 Z\u00fcrich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enrique Tom\u00e1s","family":"Mart\u00ednez Beltr\u00e1n","sequence":"additional","affiliation":[{"name":"Department of Information and Communications Engineering, University of Murcia, 30100\u2013Murcia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pedro Miguel","family":"S\u00e1nchez S\u00e1nchez","sequence":"additional","affiliation":[{"name":"Department of Information and Communications Engineering, University of Murcia, 30100\u2013Murcia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"G\u00e9r\u00f4me","family":"Bovet","sequence":"additional","affiliation":[{"name":"Cyber-Defence Campus, armasuisse Science & Technology, CH\u20133602 Thun, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Burkhard","family":"Stiller","sequence":"additional","affiliation":[{"name":"Communication Systems Group CSG, Department of Informatics, University of Zurich UZH, CH\u20138050 Z\u00fcrich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240686","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:06:57Z","timestamp":1729170417000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240686"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240686","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}