{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T17:54:46Z","timestamp":1774720486552,"version":"3.50.1"},"reference-count":48,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61932006"],"award-info":[{"award-number":["61932006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62202071"],"award-info":[{"award-number":["62202071"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018AAA0100101"],"award-info":[{"award-number":["2018AAA0100101"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2022M710518"],"award-info":[{"award-number":["2022M710518"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2022M710520"],"award-info":[{"award-number":["2022M710520"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing, China","doi-asserted-by":"publisher","award":["CSTB2022NSCQ-MSX0358"],"award-info":[{"award-number":["CSTB2022NSCQ-MSX0358"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing, China","doi-asserted-by":"publisher","award":["CSTB2022NSCQ-MSX1217"],"award-info":[{"award-number":["CSTB2022NSCQ-MSX1217"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation","award":["CNS-2153393"],"award-info":[{"award-number":["CNS-2153393"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans.Inform.Forensic Secur."],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/tifs.2023.3295949","type":"journal-article","created":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T17:37:45Z","timestamp":1689615465000},"page":"4329-4344","source":"Crossref","is-referenced-by-count":84,"title":["Privacy-Preserving Federated Learning With Malicious Clients and Honest-but-Curious Servers"],"prefix":"10.1109","volume":"18","author":[{"given":"Junqing","family":"Le","sequence":"first","affiliation":[{"name":"Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, and the College of Computer Science, Chongqing University, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4875-1319","authenticated-orcid":false,"given":"Di","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, and the College of Computer Science, Chongqing University, Chongqing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8799-7875","authenticated-orcid":false,"given":"Xinyu","family":"Lei","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Michigan Technological University, Houghton, MI, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6790-8932","authenticated-orcid":false,"given":"Long","family":"Jiao","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3279-0695","authenticated-orcid":false,"given":"Kai","family":"Zeng","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3566-8161","authenticated-orcid":false,"given":"Xiaofeng","family":"Liao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, and the College of Computer Science, Chongqing University, Chongqing, China"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"25","author":"Krizhevsky"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/SETIT.2016.7939841"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2015.2446438"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2010-343"},{"key":"ref5","article-title":"Federated optimization: Distributed optimization beyond the datacenter","author":"Kone\u010dn\u00fd","year":"2015","journal-title":"arXiv:1511.03575"},{"key":"ref6","article-title":"Federated optimization: Distributed machine learning for on-device intelligence","author":"Kone\u010dn\u00fd","year":"2016","journal-title":"arXiv:1610.02527"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134077"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/sp.2019.00065"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011544"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.3012952"},{"key":"ref11","first-page":"634","article-title":"Analyzing federated learning through an adversarial lens","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Bhagoji"},{"key":"ref12","first-page":"2938","article-title":"How to backdoor federated learning","volume-title":"Proc. Int. Conf. Artif. Intell. Statist. (AISTATS)","author":"Bagdasaryan"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133982"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2020.101889"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813687"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.2988575"},{"key":"ref17","first-page":"1","volume-title":"Learning differentially private recurrent language models","author":"McMahan","year":"2018"},{"key":"ref18","first-page":"118","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst. (NeurIPS)","author":"Blanchard"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3154503"},{"key":"ref20","first-page":"5650","article-title":"Byzantine-robust distributed learning: Towards optimal statistical rates","volume-title":"Proc. Int. Conf. Mach. Learn. (PMLR)","author":"Yin"},{"key":"ref21","article-title":"Learning to detect malicious clients for robust federated learning","author":"Li","year":"2020","journal-title":"arXiv:2002.00211"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2021.3108434"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3041404"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737416"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3000372"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.5555\/3001460.3001507"},{"key":"ref27","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Artif. Intell. Statist. (AISTATS)","author":"McMahan"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813677"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/sp.2019.00029"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.02.037"},{"key":"ref32","first-page":"301","article-title":"The limitations of federated learning in Sybil settings","volume-title":"Proc. Int. Symp. Res. Attacks, Intrusions Defenses (RAID)","author":"Fung"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1561\/9781601988195"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-79228-4_1"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref37","first-page":"263","article-title":"Bounding user contributions: A bias-variance trade-off in differential privacy","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Amin"},{"key":"ref38","first-page":"17455","article-title":"Differentially private learning with adaptive clipping","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Andrew"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/1536414.1536466"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/11761679_29"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/3068335"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2013.96"},{"key":"ref43","first-page":"1","article-title":"On the convergence of FedAvg on non-iid data","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Li"},{"key":"ref44","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref45","article-title":"Federated learning with non-iid data","author":"Zhao","year":"2018","journal-title":"arXiv:1806.00582"},{"key":"ref46","article-title":"Can you really backdoor federated learning?","author":"Sun","year":"2019","journal-title":"arXiv:1911.07963"},{"key":"ref47","first-page":"1","article-title":"Local SGD converges fast and communicates little","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Stich"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2014.2364096"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10206\/9970396\/10184496.pdf?arnumber=10184496","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T02:28:06Z","timestamp":1710383286000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10184496\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":48,"URL":"https:\/\/doi.org\/10.1109\/tifs.2023.3295949","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"value":"1556-6013","type":"print"},{"value":"1556-6021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}