{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T23:23:03Z","timestamp":1768519383475,"version":"3.49.0"},"reference-count":8,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,12,14]],"date-time":"2025-12-14T00:00:00Z","timestamp":1765670400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,14]],"date-time":"2025-12-14T00:00:00Z","timestamp":1765670400000},"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":[[2025,12,14]]},"DOI":"10.1109\/icpads67057.2025.11323144","type":"proceedings-article","created":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T20:36:54Z","timestamp":1768423014000},"page":"1-2","source":"Crossref","is-referenced-by-count":0,"title":["ProFL: A Proactive and Privacy-Preserving Client Selection Framework for Straggler Mitigation in Federated Learning"],"prefix":"10.1109","author":[{"given":"Bojin","family":"Wang","sequence":"first","affiliation":[{"name":"School of Telecommunications Engineering, Xidian University,Xi'an,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenyang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Cyber Engineering, Xidian University,Xi'an,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Cyber Engineering, Xidian University,Xi'an,China"}],"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":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics","volume":"54","author":"McMahan","year":"2017"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3025916"},{"key":"ref3","article-title":"Asynchronous federated optimization","author":"Xie","year":"2019","journal-title":"arXiv preprint"},{"key":"ref4","first-page":"19","article-title":"Oort: Efficient federated learning via guided participant selection","volume-title":"15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21)","author":"Lai","year":"2021"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2858856"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3339308"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2021.3058020"},{"key":"ref8","volume-title":"Flower: A friendly federated learning research framework","author":"Beutel","year":"2020"}],"event":{"name":"2025 IEEE 31th International Conference on Parallel and Distributed Systems (ICPADS)","location":"Hefei, China","start":{"date-parts":[[2025,12,14]]},"end":{"date-parts":[[2025,12,18]]}},"container-title":["2025 IEEE 31th International Conference on Parallel and Distributed Systems (ICPADS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11322805\/11322871\/11323144.pdf?arnumber=11323144","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T07:52:19Z","timestamp":1768463539000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11323144\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,14]]},"references-count":8,"URL":"https:\/\/doi.org\/10.1109\/icpads67057.2025.11323144","relation":{},"subject":[],"published":{"date-parts":[[2025,12,14]]}}}