{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:17:11Z","timestamp":1778080631880,"version":"3.51.4"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684369","type":"print"},{"value":"9781643684376","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T00:00:00Z","timestamp":1695859200000},"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":[[2023,9,28]]},"abstract":"<jats:p>Federated Learning (FL) is essential for building global models across distributed environments. However, it is significantly vulnerable to data and model poisoning attacks that can critically compromise the accuracy and reliability of the global model. These vulnerabilities become more pronounced in heterogeneous environments, where clients\u2019 data distributions vary broadly, creating a challenging setting for maintaining model integrity. Furthermore, malicious attacks can exploit this heterogeneity, manipulating the learning process to degrade the model or even induce it to learn incorrect patterns. In response to these challenges, we introduce RFCL, a novel Robust Federated aggregation method that leverages CLustering and cosine similarity to select similar cluster models, effectively defending against data and model poisoning attacks even amidst high data heterogeneity. Our experiments assess RFCL\u2019s performance against various attacker numbers and Non-IID degrees. The findings reveal that RFCL outperforms existing robust aggregation methods and demonstrates the capability to defend against multiple attack types.<\/jats:p>","DOI":"10.3233\/faia230257","type":"book-chapter","created":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T08:59:40Z","timestamp":1695977980000},"source":"Crossref","is-referenced-by-count":5,"title":["Robust Federated Learning Method Against Data and Model Poisoning Attacks with Heterogeneous Data Distribution"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3969-3209","authenticated-orcid":false,"given":"Ebtisaam","family":"Alharbi","sequence":"first","affiliation":[{"name":"School of Computing and Communications, Lancaster University, United Kingdom"},{"name":"Department of Computer Science, Umm Al-Qura University, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3337-8611","authenticated-orcid":false,"given":"Leandro Soriano","family":"Marcolino","sequence":"additional","affiliation":[{"name":"School of Computing and Communications, Lancaster University, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4702-3942","authenticated-orcid":false,"given":"Antonios","family":"Gouglidis","sequence":"additional","affiliation":[{"name":"School of Computing and Communications, Lancaster University, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4593-1656","authenticated-orcid":false,"given":"Qiang","family":"Ni","sequence":"additional","affiliation":[{"name":"School of Computing and Communications, Lancaster University, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2023"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230257","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T08:59:41Z","timestamp":1695977981000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230257"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,28]]},"ISBN":["9781643684369","9781643684376"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230257","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,28]]}}}