{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T06:40:06Z","timestamp":1751092806762,"version":"3.41.0"},"reference-count":20,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T00:00:00Z","timestamp":1748908800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T00:00:00Z","timestamp":1748908800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,6,3]]},"DOI":"10.1109\/eucnc\/6gsummit63408.2025.11037134","type":"proceedings-article","created":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T17:40:15Z","timestamp":1750959615000},"page":"745-750","source":"Crossref","is-referenced-by-count":0,"title":["DDFL: A Robust Clustering-Based Defense Against Poisoning Attacks in Decentralized Federated Learning"],"prefix":"10.1109","author":[{"given":"Asanka","family":"Amarasinghe","sequence":"first","affiliation":[{"name":"University of Oulu,Centre for Wireless Communications,Finland"}]},{"given":"Yushan","family":"Siriwardhana","sequence":"additional","affiliation":[{"name":"University of Oulu,Centre for Wireless Communications,Finland"}]},{"given":"Tharaka","family":"Hewa","sequence":"additional","affiliation":[{"name":"University of Oulu,Centre for Wireless Communications,Finland"}]},{"given":"Mika","family":"Ylianttila","sequence":"additional","affiliation":[{"name":"University of Oulu,Centre for Wireless Communications,Finland"}]}],"member":"263","reference":[{"volume-title":"Key drivers and research challenges for 6G ubiquitous wireless intelligence","year":"2019","author":"Latva-aho","key":"ref1"},{"doi-asserted-by":"publisher","key":"ref2","DOI":"10.1109\/mnet.001.1900287"},{"key":"ref3","article-title":"Cola: Decentralized linear learning","volume-title":"Advances in Neural Information Processing Systems","volume":"31","author":"He","year":"2018"},{"doi-asserted-by":"publisher","key":"ref4","DOI":"10.1109\/TDSC.2024.3472869"},{"doi-asserted-by":"publisher","key":"ref5","DOI":"10.1145\/3658644.3670307"},{"key":"ref6","article-title":"Jointly learning from decentralized (federated) and centralized data to mitigate distribution shift","volume-title":"Proceedings of the International Conference on Learning Representations (ICLR)","author":"Hard","year":"2021"},{"key":"ref7","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS)","volume":"54","author":"McMahan","year":"2017"},{"key":"ref8","article-title":"Communication efficiency in federated learning: Achievements and challenges","author":"Shahid","year":"2021","journal-title":"arXiv preprint arXiv"},{"doi-asserted-by":"publisher","key":"ref9","DOI":"10.1109\/EuCNC\/6GSummit60053.2024.10597004"},{"doi-asserted-by":"publisher","key":"ref10","DOI":"10.1109\/COMST.2023.3315746"},{"volume-title":"Byzantine-resilient decentralized stochastic gradient descent","year":"2021","author":"Guo","key":"ref11"},{"key":"ref12","first-page":"25044","article-title":"Collaborative learning in the jungle (decentralized, byzantine, heterogeneous, asynchronous, and nonconvex learning)","volume":"34","author":"El-Mhamdi","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref13","article-title":"Byzantine-robust decentralized learning via self-centered clipping","volume":"arXiv:2202.01545","author":"He","year":"2023","journal-title":"arXiv preprint"},{"doi-asserted-by":"publisher","key":"ref14","DOI":"10.1109\/SP46215.2023.10179291"},{"key":"ref15","article-title":"Federated learning with non-iid data","author":"Zhao","year":"2018","journal-title":"arXiv preprint arXiv"},{"key":"ref16","article-title":"Measuring the effects of non-identical data distribution for federated visual classification","volume-title":"NeurIPS Workshop on Federated Learning","author":"Hsu","year":"2019"},{"key":"ref17","first-page":"16070","article-title":"Attack of the tails: Yes, you really can backdoor federated learning","volume":"33","author":"Wang","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"doi-asserted-by":"publisher","key":"ref18","DOI":"10.1109\/ICCV51070.2023.00461"},{"key":"ref19","first-page":"281","article-title":"Some methods for classification and analysis of multivariate observations","volume-title":"Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability","volume":"1","author":"MacQueen","year":"1967"},{"doi-asserted-by":"publisher","key":"ref20","DOI":"10.1109\/5.726791"}],"event":{"name":"2025 Joint European Conference on Networks and Communications & 6G Summit (EuCNC\/6G Summit)","start":{"date-parts":[[2025,6,3]]},"location":"Poznan, Poland","end":{"date-parts":[[2025,6,6]]}},"container-title":["2025 Joint European Conference on Networks and Communications & 6G Summit (EuCNC\/6G Summit)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11036818\/11036731\/11037134.pdf?arnumber=11037134","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T06:10:46Z","timestamp":1751091046000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11037134\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,3]]},"references-count":20,"URL":"https:\/\/doi.org\/10.1109\/eucnc\/6gsummit63408.2025.11037134","relation":{},"subject":[],"published":{"date-parts":[[2025,6,3]]}}}