{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T06:38:06Z","timestamp":1761979086359,"version":"build-2065373602"},"reference-count":21,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:00:00Z","timestamp":1760486400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T00:00:00Z","timestamp":1760486400000},"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":[[2025,10,15]]},"DOI":"10.1109\/ccci65983.2025.11215089","type":"proceedings-article","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T17:09:54Z","timestamp":1761930594000},"page":"1-8","source":"Crossref","is-referenced-by-count":0,"title":["Trust-Aware Reinforcement Selection for Robust Federated Learning under Adaptive Adversaries"],"prefix":"10.1109","author":[{"given":"Shafiq","family":"Ahmed","sequence":"first","affiliation":[{"name":"University of Essex,School of Computer Science and Electronic Engineering,Colchester,UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad S.","family":"Obaidat","sequence":"additional","affiliation":[{"name":"University of Jordan,King Abdullah II School of Information Technology,Amman,Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Hossein Anisi","sequence":"additional","affiliation":[{"name":"University of Essex,School of Computer Science and Electronic Engineering,Colchester,UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khalid","family":"Mahmood","sequence":"additional","affiliation":[{"name":"National Yunlin University of Science and Technology,Graduate School of Intelligent Data Science,Douliu,Taiwan,64002"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","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":"ref2","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":"ref3","first-page":"2938","article-title":"How to backdoor federated learning","volume-title":"International conference on artificial intelligence and statistics","author":"Bagdasaryan"},{"key":"ref4","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":"ref5","first-page":"5650","article-title":"Byzantine-robust distributed learning: Towards optimal statistical rates","volume-title":"International conference on machine learning","author":"Yin"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3216981"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/SEAMS66627.2025.00026"},{"article-title":"Poisoning attacks against support vector machines","year":"2012","author":"Biggio","key":"ref8"},{"key":"ref9","first-page":"634","article-title":"Analyzing federated learning through an adversarial lens","volume-title":"International conference on machine learning","author":"Bhagoji"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2021.24434"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ISSRE55969.2022.00044"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/MASS58611.2023.00068"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-96-0151-6_7"},{"key":"ref14","first-page":"301","article-title":"The limitations of federated learning in sybil settings","volume-title":"23rd International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2020)","author":"Fung"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM48099.2022.10001718"},{"key":"ref16","first-page":"1415","article-title":"{FLAME}: Taming backdoors in federated learning","volume-title":"31st USENIX security symposium (USENIX Security 22)","author":"Nguyen"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-88418-5_22"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3488932.3517395"},{"article-title":"Federated select: A primitive for communication-and memory-efficient federated learning","year":"2022","author":"Charles","key":"ref19"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3299573"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2024.3361451"}],"event":{"name":"2025 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)","start":{"date-parts":[[2025,10,15]]},"location":"Hangzhou, China","end":{"date-parts":[[2025,10,17]]}},"container-title":["2025 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11214500\/11215083\/11215089.pdf?arnumber=11215089","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T06:34:02Z","timestamp":1761978842000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11215089\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,15]]},"references-count":21,"URL":"https:\/\/doi.org\/10.1109\/ccci65983.2025.11215089","relation":{},"subject":[],"published":{"date-parts":[[2025,10,15]]}}}