{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T01:48:04Z","timestamp":1773020884279,"version":"3.50.1"},"reference-count":25,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T00:00:00Z","timestamp":1676851200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T00:00:00Z","timestamp":1676851200000},"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":[[2023,2,20]]},"DOI":"10.1109\/icnc57223.2023.10074494","type":"proceedings-article","created":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T17:39:46Z","timestamp":1679593186000},"page":"66-70","source":"Crossref","is-referenced-by-count":8,"title":["Byzantine-Resilient Federated Learning With Differential Privacy Using Online Mirror Descent"],"prefix":"10.1109","author":[{"given":"Olusola T.","family":"Odeyomi","sequence":"first","affiliation":[{"name":"North Carolina A & T University,Department of Computer Science,Greensboro,NC,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gergely","family":"Zaruba","sequence":"additional","affiliation":[{"name":"University of North Texas,School of Computing,Denton,TX,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017","journal-title":"Artificial Intelligence and Statistics. PMLR"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2018.01.007"},{"key":"ref3","article-title":"Peer-to-peer federated learning on graphs","author":"Lalitha","year":"2019","journal-title":"arXiv preprint arXiv:1901.11173"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/AIIoT52608.2021.9454170"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT45174.2021.9518177"},{"key":"ref6","article-title":"Communication trade-offs for synchronized distributed sgd with large step size","author":"Patel","year":"2019","journal-title":"arXiv preprint arXiv:1904.11325"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2978082"},{"key":"ref8","article-title":"Gossip dual averaging for decentralized optimization of pairwise functions","author":"Colin","year":"2016","journal-title":"arXiv preprint arXiv:1606.02421"},{"key":"ref9","article-title":"Fedat: A communication-efficient federated learning method with asynchronous tiers under non-iid data","author":"Chai","year":"2020","journal-title":"ArXivorg"},{"key":"ref10","article-title":"Machine learning with adversaries: Byzantine tolerant gradient descent","volume":"30","author":"Blanchard","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref11","article-title":"Mitigating byzantine attacks in federated learning","author":"Prakash","year":"2020","journal-title":"arXiv preprint arXiv:2010.07541"},{"key":"ref12","first-page":"1605","article-title":"Local model poisoning attacks to \\{Byzantine-Robust\\} federated learning","volume-title":"29th USENIX Security Symposium (USENIX Security 20)","author":"Fang"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2020.3041404"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/670"},{"key":"ref15","article-title":"Challenges and approaches for mitigating byzantine attacks in federated learning","author":"Hu","year":"2021","journal-title":"arXiv preprint arXiv:2112.14468"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2020.XVI.021"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2017.2743462"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2018.2823901"},{"key":"ref19","article-title":"Federated learning with non-iid data","author":"Zhao","year":"2018","journal-title":"arXiv preprint arXiv:1806.00582"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-79228-4_1"},{"key":"ref21","first-page":"24","article-title":"Differentially private online learning","volume-title":"Conference on Learning Theory","author":"Jain"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TSIPN.2019.2957731"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.comgeo.2010.04.006"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1090\/bull\/1653"},{"key":"ref25","article-title":"On the convergence of fedavg on non-iid data","author":"Li","year":"2019","journal-title":"arXiv preprint arXiv:1907.02189"}],"event":{"name":"2023 International Conference on Computing, Networking and Communications (ICNC)","location":"Honolulu, HI, USA","start":{"date-parts":[[2023,2,20]]},"end":{"date-parts":[[2023,2,22]]}},"container-title":["2023 International Conference on Computing, Networking and Communications (ICNC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10073968\/10073976\/10074494.pdf?arnumber=10074494","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T17:02:19Z","timestamp":1707843739000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10074494\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,20]]},"references-count":25,"URL":"https:\/\/doi.org\/10.1109\/icnc57223.2023.10074494","relation":{},"subject":[],"published":{"date-parts":[[2023,2,20]]}}}