{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T04:08:32Z","timestamp":1741838912761,"version":"3.38.0"},"reference-count":18,"publisher":"IEEE","license":[{"start":{"date-parts":[[2024,12,8]],"date-time":"2024-12-08T00:00:00Z","timestamp":1733616000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,12,8]],"date-time":"2024-12-08T00:00:00Z","timestamp":1733616000000},"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":[[2024,12,8]]},"DOI":"10.1109\/globecom52923.2024.10901479","type":"proceedings-article","created":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T17:30:35Z","timestamp":1741714235000},"page":"1761-1766","source":"Crossref","is-referenced-by-count":0,"title":["FedPro: Protecting Federated Learning from Malicious Participants in Internet of Vehicles"],"prefix":"10.1109","author":[{"given":"Jiacheng","family":"Xu","sequence":"first","affiliation":[{"name":"Guangdong University of Technology,School of Information Engineering,Guangzhou,China,510006"}]},{"given":"Chang","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangdong University of Technology,School of Information Engineering,Guangzhou,China,510006"}]},{"given":"Hongmin","family":"Wei","sequence":"additional","affiliation":[{"name":"Guangdong University of Technology,School of Information Engineering,Guangzhou,China,510006"}]},{"given":"Yurong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Guangdong University of Technology,School of Information Engineering,Guangzhou,China,510006"}]},{"given":"Chunchao","family":"Lane","sequence":"additional","affiliation":[{"name":"St. Mary&#x2019;s College of Maryland,Department of Mathematics and Computer Science,St. Mary&#x2019;s City,MD,USA,20686"}]},{"given":"Guojun","family":"Han","sequence":"additional","affiliation":[{"name":"Guangdong University of Technology,School of Information Engineering,Guangzhou,China,510006"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICC45041.2023.10279392"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2023.3316643"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3013541"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/DICCT56244.2023.10110075"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3269980"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2015.2422735"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICCC57788.2023.10233464"},{"key":"ref8","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Artificial intelligence and statistics.","author":"McMahan","year":"2017"},{"key":"ref9","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proceedings of Machine Learning and Systems","volume":"2","author":"Li"},{"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","first-page":"5650","article-title":"Byzantine-robust distributed learning: Towards optimal statistical rates","volume-title":"International Conference on Machine Learning","author":"Yin"},{"key":"ref12","first-page":"3521","article-title":"The hidden vulnerability of distributed learning in byzantium","volume-title":"International Conference on Machine Learning","author":"Guerraoui"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/DSC61021.2023.10354149"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2022.102819"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.17"},{"key":"ref16","first-page":"2938","article-title":"How to backdoor federated learning","volume-title":"Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics","volume":"108","author":"Bagdasaryan"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2010.133"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2023.3306547"}],"event":{"name":"GLOBECOM 2024 - 2024 IEEE Global Communications Conference","start":{"date-parts":[[2024,12,8]]},"location":"Cape Town, South Africa","end":{"date-parts":[[2024,12,12]]}},"container-title":["GLOBECOM 2024 - 2024 IEEE Global Communications Conference"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10900933\/10900934\/10901479.pdf?arnumber=10901479","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T05:54:09Z","timestamp":1741758849000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10901479\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,8]]},"references-count":18,"URL":"https:\/\/doi.org\/10.1109\/globecom52923.2024.10901479","relation":{},"subject":[],"published":{"date-parts":[[2024,12,8]]}}}