{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T09:37:23Z","timestamp":1773999443334,"version":"3.50.1"},"reference-count":24,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"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":[[2025,12,8]]},"DOI":"10.1109\/globecom59602.2025.11432611","type":"proceedings-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T20:04:01Z","timestamp":1773950641000},"page":"1754-1759","source":"Crossref","is-referenced-by-count":0,"title":["Towards a Larger Model via One-Shot Federated Learning on Heterogeneous Client Models"],"prefix":"10.1109","author":[{"given":"Wenxuan","family":"Ye","sequence":"first","affiliation":[{"name":"Huawei Technologies Duesseldorf GmbH,Advanced Wireless Technology Laboratory, Munich Research Center"}]},{"given":"Xueli","family":"An","sequence":"additional","affiliation":[{"name":"Huawei Technologies Duesseldorf GmbH,Advanced Wireless Technology Laboratory, Munich Research Center"}]},{"given":"Onur","family":"Ayan","sequence":"additional","affiliation":[{"name":"Huawei Technologies Duesseldorf GmbH,Advanced Wireless Technology Laboratory, Munich Research Center"}]},{"given":"Junfan","family":"Wang","sequence":"additional","affiliation":[{"name":"Huawei Technologies Co., Ltd,Wireless Technology Lab, 2012 Laboratories"}]},{"given":"Xueqiang","family":"Yan","sequence":"additional","affiliation":[{"name":"Huawei Technologies Co., Ltd,Wireless Technology Lab, 2012 Laboratories"}]},{"given":"Georg","family":"Carle","sequence":"additional","affiliation":[{"name":"Technical University of Munich,TUM School of Computation, Information and Technology"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Language models are few-shot learners","author":"Brown","year":"2020","journal-title":"NeurIPS"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-025-09422-z"},{"key":"ref3","article-title":"Why do larger models generalize better? A theoretical perspective via the XOR problem","volume-title":"ICML","author":"Brutzkus"},{"key":"ref4","article-title":"Ordered subgraph aggregation networks","author":"Qian","year":"2022","journal-title":"NeurIPS"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1561\/9781680837896"},{"key":"ref6","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"AISTATS","author":"McMahan"},{"key":"ref7","article-title":"Federated optimization in heterogeneous networks","author":"Li","year":"2020","journal-title":"MLSys"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM48099.2022.10001307"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM54140.2023.10436772"},{"key":"ref10","article-title":"Distilling the knowledge in a neural network","author":"Hinton","year":"2015"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/399"},{"key":"ref12","article-title":"Fedgems: Federated learning of larger server models via selective knowledge fusion","author":"Cheng","year":"2021"},{"key":"ref13","article-title":"Ensemble distillation for robust model fusion in federated learning","author":"Lin","year":"2020","journal-title":"NeurIPS"},{"key":"ref14","article-title":"Not all knowledge is created equal: Mutual distillation of confident knowledge","volume-title":"NeurIPS workshop","author":"Li"},{"key":"ref15","article-title":"Personalized federated learning for heterogeneous clients with clustered knowledge transfer","author":"Cho","year":"2021"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM53939.2023.10228954"},{"key":"ref17","article-title":"One-shot federated learning","author":"Guha","year":"2019"},{"key":"ref18","article-title":"Dense: Data-free one-shot federated learning","author":"Zhang","year":"2022","journal-title":"NeurIPS"},{"key":"ref19","article-title":"Towards addressing label skews in one-shot federated learning","volume-title":"ICLR","author":"Diao"},{"key":"ref20","article-title":"Semi-supervised learning by entropy minimization","author":"Grandvalet","year":"2004","journal-title":"NeurIPS"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v29i1.9608"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00019"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/205"}],"event":{"name":"GLOBECOM 2025 - 2025 IEEE Global Communications Conference","location":"Taipei, Taiwan","start":{"date-parts":[[2025,12,8]]},"end":{"date-parts":[[2025,12,12]]}},"container-title":["GLOBECOM 2025 - 2025 IEEE Global Communications Conference"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11431620\/11431622\/11432611.pdf?arnumber=11432611","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T06:05:53Z","timestamp":1773986753000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11432611\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,8]]},"references-count":24,"URL":"https:\/\/doi.org\/10.1109\/globecom59602.2025.11432611","relation":{},"subject":[],"published":{"date-parts":[[2025,12,8]]}}}