{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T12:43:21Z","timestamp":1746708201346,"version":"3.28.0"},"reference-count":17,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T00:00:00Z","timestamp":1701648000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,12,4]],"date-time":"2023-12-04T00:00:00Z","timestamp":1701648000000},"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":[[2023,12,4]]},"DOI":"10.1109\/globecom54140.2023.10437587","type":"proceedings-article","created":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T19:45:36Z","timestamp":1708976736000},"page":"595-600","source":"Crossref","is-referenced-by-count":1,"title":["AFL: Attention-Based Byzantine-Robust Federated Learning with Vector Filter"],"prefix":"10.1109","author":[{"given":"Hao","family":"Chen","sequence":"first","affiliation":[{"name":"School of Cyber Engineering, Xidian University,Xi&#x0027;an,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xixiang","family":"Lv","sequence":"additional","affiliation":[{"name":"Xidian University,National Key Lab of ISN,Xi&#x0027;an,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Cyber Engineering, Xidian University,Xi&#x0027;an,China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"14671474","article-title":"Poisoning attacks against support vector machines","volume-title":"Proc. the 29th International Coference on International Conference on Machine Learning (ICML12)","author":"Biggio","year":"2012"},{"key":"ref2","first-page":"2938","article-title":"How to backdoor federated learning","volume-title":"Proc. International Conference on Artificial Intelligence and Statistics (AISTATS20)","volume":"108","author":"Bagdasaryan","year":"2020"},{"journal-title":"Analyzing federated learning through an adversarial lens","year":"2018","author":"Bhagoji","key":"ref3"},{"key":"ref4","article-title":"Local model poisoning attacks to byzantine-robust federated learning","volume-title":"Proc. the 29th USENIX Conference on Security Symposium (USENIX20)","author":"Fang","year":"2020"},{"key":"ref5","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","volume-title":"Proc. Neural Information Processing Systems (NIPS17)","volume":"30","author":"Blanchard","year":"2017"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3154503"},{"journal-title":"The hidden vulnera-bility of distributed learning in byzantium","year":"2018","author":"Mhamdi","key":"ref7"},{"key":"ref8","first-page":"5650","article-title":"Byzantine-robust distributed learning: Towards optimal statistical rates","volume-title":"International Conference on Machine Learning (ICML18)","author":"Yin","year":"2018"},{"key":"ref9","article-title":"A little is enough: Circumventing defenses for distributed learning","volume":"32","author":"Baruch","year":"2019","journal-title":"Advances in Neural Information Processing Systems (NIPS19)"},{"key":"ref10","first-page":"560","article-title":"signsgd: Compressed optimisation for non-convex problems","volume-title":"International Conference on Machine Learning (ICML18)","author":"Bernstein","year":"2018"},{"key":"ref11","first-page":"10495","article-title":"Zeno++: Robust fully asynchronous s-gd","volume-title":"Proc. International Conference on Machine Learning (ICML20)","author":"Xie","year":"2020"},{"key":"ref12","article-title":"Byzantine stochastic gradient de-scent","volume":"31","author":"Alistarh","year":"2018","journal-title":"Advances in Neural Information Processing Systems (NIPS18)"},{"journal-title":"Byzantine-resilient non-convex stochastic gradient descent","year":"2020","author":"Allen-Zhu","key":"ref13"},{"key":"ref14","article-title":"Detox: A redundancy-based framework for faster and more robust gradient aggre-gation","volume":"32","author":"Rajput","year":"2019","journal-title":"Advances in Neural Information Processing Systems (NIPS19)"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2021.3108434"},{"journal-title":"ADADELTA: an adaptive learning rate method","year":"2012","author":"Zeiler","key":"ref16"},{"key":"ref17","first-page":"3521","article-title":"The hidden vul-nerability of distributed learning in Byzantium","volume-title":"Proc. International Conference on Machine Learning (ICML18)","volume":"80","author":"El Mhamdi","year":"2018"}],"event":{"name":"GLOBECOM 2023 - 2023 IEEE Global Communications Conference","start":{"date-parts":[[2023,12,4]]},"location":"Kuala Lumpur, Malaysia","end":{"date-parts":[[2023,12,8]]}},"container-title":["GLOBECOM 2023 - 2023 IEEE Global Communications Conference"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10436708\/10436716\/10437587.pdf?arnumber=10437587","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T01:39:34Z","timestamp":1709257174000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10437587\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,4]]},"references-count":17,"URL":"https:\/\/doi.org\/10.1109\/globecom54140.2023.10437587","relation":{},"subject":[],"published":{"date-parts":[[2023,12,4]]}}}