{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T22:11:32Z","timestamp":1783721492832,"version":"3.55.0"},"reference-count":26,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,4,6]],"date-time":"2025-04-06T00:00:00Z","timestamp":1743897600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,4,6]],"date-time":"2025-04-06T00:00:00Z","timestamp":1743897600000},"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":[[2025,4,6]]},"DOI":"10.1109\/icassp49660.2025.10889428","type":"proceedings-article","created":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T17:15:02Z","timestamp":1741799702000},"page":"1-5","source":"Crossref","is-referenced-by-count":11,"title":["LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Data"],"prefix":"10.1109","author":[{"given":"Yuxin","family":"Zhang","sequence":"first","affiliation":[{"name":"Fudan University,School of Computer Science,Shanghai,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Fudan University,School of Computer Science,Shanghai,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Lin","sequence":"additional","affiliation":[{"name":"The University of Hong Kong,Department of Electrical and Electronic Engineering,Hong Kong,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Chen","sequence":"additional","affiliation":[{"name":"Fudan University,School of Computer Science,Shanghai,China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Fudan University,School of Computer Science,Shanghai,China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TASLP.2024.3399607"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.56553\/popets-2024-0074"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3189905"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2018.8486403"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2024.3359040"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1126\/science.aar5419"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM53939.2023.10228876"},{"key":"ref8","first-page":"1273","article-title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","volume-title":"Proc. of the 20th AISTATS","author":"McMahan"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP48485.2024.10446033"},{"key":"ref10","first-page":"10752","article-title":"On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond","volume-title":"Proc. of the 36th NIPS","author":"Yuan"},{"key":"ref11","article-title":"Federated Optimization in Heterogeneous Networks","volume-title":"Proc. MLSys","author":"Li"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3015958"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-022-01046-x"},{"key":"ref14","first-page":"19586","article-title":"An Efficient Framework for Clustered Federated Learning","volume-title":"Proc. of the 34th NIPS","author":"Ghosh"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/311"},{"key":"ref16","article-title":"Exploiting Shared Representations for Personalized Federated Learning","volume-title":"Proc. of the 38th ICML","author":"Collins"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20819"},{"key":"ref18","first-page":"1269","article-title":"Aggregating Local Deep Features for Image Retrieval","volume-title":"Proc. of the 15th ICCV","author":"Babenko"},{"key":"ref19","first-page":"5972","article-title":"No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-iid Data","volume-title":"Proc. of the 35th NeurIPS","author":"Luo"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207469"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00042"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/0169-7439(87)80084-9"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref24","article-title":"Learning Multiple lLayers of Features from Tiny Images","author":"Krizhevsky","year":"2009"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM48880.2022.9796724"},{"key":"ref26","article-title":"Federated Learning with Personalization Layers","author":"Arivazhagan","year":"2019"}],"event":{"name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","location":"Hyderabad, India","start":{"date-parts":[[2025,4,6]]},"end":{"date-parts":[[2025,4,11]]}},"container-title":["ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10887540\/10887541\/10889428.pdf?arnumber=10889428","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T05:26:25Z","timestamp":1774416385000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10889428\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,6]]},"references-count":26,"URL":"https:\/\/doi.org\/10.1109\/icassp49660.2025.10889428","relation":{},"subject":[],"published":{"date-parts":[[2025,4,6]]}}}