{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T18:28:47Z","timestamp":1771698527570,"version":"3.50.1"},"reference-count":60,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key R&D Program of China","award":["2021YFA0715202"],"award-info":[{"award-number":["2021YFA0715202"]}]},{"name":"Shenzhen Key Laboratory of Ubiquitous Data Enabling","award":["ZDSYS20220527171406015"],"award-info":[{"award-number":["ZDSYS20220527171406015"]}]},{"name":"Shenzhen Science and Technology Program","award":["KQTD20170810150821146"],"award-info":[{"award-number":["KQTD20170810150821146"]}]},{"name":"Research Grants Council of the Hong Kong SAR","award":["GRF11217823"],"award-info":[{"award-number":["GRF11217823"]}]},{"name":"Collaborative Research Fund","award":["C1042-23GF"],"award-info":[{"award-number":["C1042-23GF"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62371411"],"award-info":[{"award-number":["62371411"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"InnoHK initiative"},{"name":"Government of the HKSAR"},{"name":"Laboratory for AI-Powered Financial Technologies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Sel. Top. Signal Process."],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1109\/jstsp.2024.3497660","type":"journal-article","created":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T18:53:31Z","timestamp":1731524011000},"page":"195-207","source":"Crossref","is-referenced-by-count":3,"title":["Cooperative Multi-Model Training for Personalized Federated Learning Over Heterogeneous Devices"],"prefix":"10.1109","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6201-9215","authenticated-orcid":false,"given":"Jian","family":"Xu","sequence":"first","affiliation":[{"name":"Tsinghua Shenzhen International Graduate School, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3350-3929","authenticated-orcid":false,"given":"Shuo","family":"Wan","sequence":"additional","affiliation":[{"name":"Noah's Ark Lab, Huawei Technologies, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4263-5130","authenticated-orcid":false,"given":"Yinchuan","family":"Li","sequence":"additional","affiliation":[{"name":"Noah's Ark Lab, Huawei Technologies, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8753-9137","authenticated-orcid":false,"given":"Sichun","family":"Luo","sequence":"additional","affiliation":[{"name":"Shenzhen Research Institute, City University of Hong Kong, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhilin","family":"Chen","sequence":"additional","affiliation":[{"name":"Noah's Ark Lab, Huawei Technologies, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4335-5157","authenticated-orcid":false,"given":"Yunfeng","family":"Shao","sequence":"additional","affiliation":[{"name":"Noah's Ark Lab, Huawei Technologies, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhitang","family":"Chen","sequence":"additional","affiliation":[{"name":"Noah's Ark Lab, Huawei Technologies, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2827-4022","authenticated-orcid":false,"given":"Shao-Lun","family":"Huang","sequence":"additional","affiliation":[{"name":"Tsinghua Shenzhen International Graduate School, Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2756-4984","authenticated-orcid":false,"given":"Linqi","family":"Song","sequence":"additional","affiliation":[{"name":"Shenzhen Research Institute, City University of Hong Kong, Hong Kong"}],"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":"Proc. Artif. Intell. Statist.","author":"McMahan","year":"2017"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3095077"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i11.21505"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3501296"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557668"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3664927"},{"key":"ref9","article-title":"Decentralized federated learning of deep neural networks on non-IID data","volume-title":"Proc. Int. Workshop Federated Learn. User Privacy Data Confidentiality","author":"Onoszko","year":"2021"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TSIPN.2022.3151242"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3196503"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1561\/9781680837896"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2986024"},{"key":"ref14","article-title":"Federated learning with non-IID data","author":"Zhao","year":"2018"},{"key":"ref15","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst.","volume":"2","author":"Li","year":"2020"},{"key":"ref16","first-page":"3581","article-title":"Federated learning with buffered asynchronous aggregation","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Nguyen","year":"2022"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2904348"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3447993.3483278"},{"key":"ref19","first-page":"29677","article-title":"FedRolex: Model-heterogeneous federated learning with rolling sub-model extraction","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Alam","year":"2022"},{"key":"ref20","article-title":"FedMD: Heterogenous federated learning via model distillation","volume-title":"Proc. Int. Workshop Federated Learn. Data Privacy Confidentiality","author":"Li","year":"2019"},{"key":"ref21","first-page":"14860","article-title":"Architecture agnostic federated learning for neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Makhija","year":"2022"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3160699"},{"key":"ref23","first-page":"35684","article-title":"Factorized-FL: Personalized federated learning with parameter factorization & similarity matching","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Jeong","year":"2022"},{"key":"ref24","first-page":"2089","article-title":"Exploiting shared representations for personalized federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Collins","year":"2021"},{"key":"ref25","article-title":"Adaptive personalized federated learning","author":"Deng","year":"2020"},{"key":"ref26","article-title":"On bridging generic and personalized federated learning for image classification","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Chen","year":"2022"},{"key":"ref27","first-page":"4427","article-title":"Federated multi-task learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Smith","year":"2017"},{"key":"ref28","first-page":"21394","article-title":"Personalized federated learning with moreau envelopes","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Dinh","year":"2020"},{"key":"ref29","first-page":"6357","article-title":"Ditto: Fair and robust federated learning through personalization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li","year":"2021"},{"key":"ref30","article-title":"Federated learning with personalization layers","author":"Arivazhagan","year":"2019"},{"key":"ref31","article-title":"Think locally, act globally: Federated learning with local and global representations","volume-title":"Proc. Int. Workshop Federated Learn. Data Privacy Confidentiality","author":"Liang","year":"2019"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN55064.2022.9892585"},{"key":"ref33","article-title":"Personalized federated learning with feature alignment and classifier collaboration","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Xu","year":"2023"},{"key":"ref34","article-title":"Personalized federated learning with first order model optimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhang","year":"2021"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/301"},{"key":"ref36","first-page":"15434","article-title":"Federated multi-task learning under a mixture of distributions","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Marfoq","year":"2021"},{"key":"ref37","first-page":"37860","article-title":"Personalized federated learning under mixture of distributions","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wu","year":"2023"},{"key":"ref38","first-page":"4587","article-title":"DisPFL: Towards communication-efficient personalized federated learning via decentralized sparse training","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Dai","year":"2022"},{"key":"ref39","first-page":"19586","article-title":"An efficient framework for clustered federated learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Ghosh","year":"2020"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3015958"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00990"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/3624062.3626325"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2022.3231527"},{"key":"ref44","first-page":"8432","article-title":"FedProto: Federated prototype learning over heterogeneous devices","volume-title":"Proc. AAAI Conf. Artif. Intell.","author":"Tan","year":"2022"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3611781"},{"key":"ref46","article-title":"HeteroFL: Computation and communication efficient federated learning for heterogeneous clients","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Diao","year":"2021"},{"key":"ref47","first-page":"12876","article-title":"FjORD: Fair and accurate federated learning under heterogeneous targets with ordered dropout","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Horvth","year":"2021"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1145\/3485730.3485929"},{"key":"ref49","first-page":"592","article-title":"TailorFL: Dual-personalized federated learning under system and data heterogeneity","volume-title":"Proc. 20th ACM Conf. Embedded Networked Sensor Syst.","author":"Deng","year":"2023"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1631\/FITEE.2300098"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-29763-x"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599344"},{"key":"ref53","first-page":"5972","article-title":"No fear of heterogeneity: Classifier calibration for federated learning with non-IID data","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Luo","year":"2021"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2021.3128679"},{"key":"ref55","first-page":"17716","article-title":"Federated learning with partial model personalization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Pillutla","year":"2022"},{"key":"ref56","article-title":"Towards understanding ensemble, knowledge distillation and self-distillation in deep learning","volume-title":"Proc. 11th Int. Conf. Learn. Representations","author":"Allen-Zhu","year":"2023"},{"key":"ref57","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017"},{"key":"ref58","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref60","article-title":"FedBABU: Toward enhanced representation for federated image classification","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Oh","year":"2022"}],"container-title":["IEEE Journal of Selected Topics in Signal Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/4200690\/10906680\/10752386.pdf?arnumber=10752386","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T17:44:21Z","timestamp":1749663861000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10752386\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":60,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/jstsp.2024.3497660","relation":{},"ISSN":["1932-4553","1941-0484"],"issn-type":[{"value":"1932-4553","type":"print"},{"value":"1941-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]}}}