{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T10:15:58Z","timestamp":1784369758773,"version":"3.55.0"},"reference-count":51,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Science and Technology Innovation Plan Of Shanghai Science","award":["24BC3200600"],"award-info":[{"award-number":["24BC3200600"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Dependable and Secure Comput."],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1109\/tdsc.2025.3542437","type":"journal-article","created":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T13:43:49Z","timestamp":1739799829000},"page":"3993-4009","source":"Crossref","is-referenced-by-count":16,"title":["FLAD: Byzantine-Robust Federated Learning Based on Gradient Feature Anomaly Detection"],"prefix":"10.1109","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6607-1280","authenticated-orcid":false,"given":"Peng","family":"Tang","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9504-5717","authenticated-orcid":false,"given":"Xiaoyu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6428-1655","authenticated-orcid":false,"given":"Weidong","family":"Qiu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Huang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenyu","family":"Mu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5628-7328","authenticated-orcid":false,"given":"Shujun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computing &amp; Institute of Cyber Security for Society (iCSS), University of Kent, Canterbury, U.K."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2022.3216981"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.2977378"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2021.3108434"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2021.24434"},{"key":"ref7","first-page":"11372","article-title":"CRFL: Certifiably robust federated learning against backdoor attacks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xie"},{"key":"ref8","first-page":"1415","article-title":"FLAME: Taming backdoors in federated learning","volume-title":"Proc. 31st USENIX Secur. Symp.","author":"Nguyen"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-70694-8_15"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8683121"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.3012952"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011544"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00383"},{"key":"ref14","first-page":"1605","article-title":"Local model poisoning attacks to byzantine-robust federated learning","volume-title":"Proc. 29th USENIX Secur. Symp.","author":"Fang"},{"key":"ref15","doi-asserted-by":"crossref","DOI":"10.14722\/ndss.2021.24498","article-title":"Manipulating the byzantine: Optimizing model poisoning attacks and defenses for federated learning","volume-title":"Proc. Netw. Distrib. System Secur. Symp.","author":"Shejwalkar"},{"key":"ref16","article-title":"Poisoning attacks against support vector machines","author":"Biggio","year":"2012"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58951-6_24"},{"key":"ref18","first-page":"2938","article-title":"How to backdoor federated learning","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Bagdasaryan"},{"key":"ref19","article-title":"Can you really backdoor federated learning?","author":"Sun","year":"2019"},{"key":"ref20","article-title":"DBA: Distributed backdoor attacks against federated learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xie"},{"key":"ref21","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. 20th Int. Conf. Arti. Intelli. Statist.","author":"McMahan"},{"key":"ref22","first-page":"118","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Blanchard"},{"key":"ref23","first-page":"5650","article-title":"Byzantine-robust distributed learning: Towards optimal statistical rates","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yin"},{"key":"ref24","first-page":"3521","article-title":"The hidden vulnerability of distributed learning in byzantium","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Guerraoui"},{"key":"ref25","first-page":"10320","article-title":"DETOX: A redundancy-based framework for faster and more robust gradient aggregation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Rajput"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2022.3153135"},{"key":"ref27","first-page":"634","article-title":"Analyzing federated learning through an adversarial lens","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Bhagoji"},{"key":"ref28","article-title":"signSGD with majority vote is communication efficient and fault tolerant","author":"Bernstein","year":"2018"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.14711\/thesis-991012994506603412"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-010-5188-5"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2018.00057"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539231"},{"key":"ref33","first-page":"508","article-title":"AUROR: Defending against poisoning attacks in collaborative deep learning systems","volume-title":"Proc. 32nd Annu. Conf. Comput. Secur. Appl.","author":"Shen"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-16-7167-8_50"},{"key":"ref35","doi-asserted-by":"crossref","DOI":"10.14722\/ndss.2022.23156","article-title":"DeepSight: Mitigating backdoor attacks in federated learning through deep model inspection","volume-title":"Proc. Netw. Distrib. System Secur. Symp.","author":"Rieger"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3068335"},{"key":"ref37","article-title":"Adadelta: An adaptive learning rate method","author":"Zeiler","year":"2012"},{"key":"ref38","article-title":"Mnist handwritten digit database","author":"LeCun","year":"1998"},{"key":"ref39","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref40","article-title":"LEAF: A benchmark for federated settings","author":"Caldas","year":"2018"},{"key":"ref41","article-title":"Explaining and harnessing adversarial examples","author":"Goodfellow","year":"2014"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_2"},{"key":"ref43","article-title":"IDLG: Improved deep leakage from gradients","author":"Zhao","year":"2020"},{"issue":"4","key":"ref44","first-page":"1","volume":"13","author":"Ren","year":"2022","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/SPW53761.2021.00017"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3041404"},{"key":"ref47","first-page":"17455","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Andrew"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/324"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.09.064"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2020.3005909"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00065"}],"container-title":["IEEE Transactions on Dependable and Secure Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/8858\/11077775\/10891051.pdf?arnumber=10891051","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T22:48:47Z","timestamp":1752274127000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10891051\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7]]},"references-count":51,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tdsc.2025.3542437","relation":{},"ISSN":["1545-5971","1941-0018","2160-9209"],"issn-type":[{"value":"1545-5971","type":"print"},{"value":"1941-0018","type":"electronic"},{"value":"2160-9209","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7]]}}}