{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T09:03:30Z","timestamp":1773219810380,"version":"3.50.1"},"reference-count":44,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Key-Area Research and Development Program of Guangdong Province","award":["2021B0101400003"],"award-info":[{"award-number":["2021B0101400003"]}]},{"name":"Hong Kong RGC Research Impact Fund","award":["R5060-19"],"award-info":[{"award-number":["R5060-19"]}]},{"name":"Hong Kong RGC Research Impact Fund","award":["R5034-18"],"award-info":[{"award-number":["R5034-18"]}]},{"name":"Areas of Excellence Scheme","award":["AoE\/E-601\/22-R"],"award-info":[{"award-number":["AoE\/E-601\/22-R"]}]},{"name":"General Research Fund","award":["152203\/20E"],"award-info":[{"award-number":["152203\/20E"]}]},{"name":"General Research Fund","award":["152244\/21E"],"award-info":[{"award-number":["152244\/21E"]}]},{"name":"General Research Fund","award":["152169\/22E"],"award-info":[{"award-number":["152169\/22E"]}]},{"name":"General Research Fund","award":["152228\/23E"],"award-info":[{"award-number":["152228\/23E"]}]},{"DOI":"10.13039\/501100010877","name":"Shenzhen Science and Technology Innovation Commission","doi-asserted-by":"publisher","award":["JCYJ20200109142008673"],"award-info":[{"award-number":["JCYJ20200109142008673"]}],"id":[{"id":"10.13039\/501100010877","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62102022"],"award-info":[{"award-number":["62102022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Mobile Comput."],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1109\/tmc.2024.3396218","type":"journal-article","created":{"date-parts":[[2024,5,2]],"date-time":"2024-05-02T17:32:09Z","timestamp":1714671129000},"page":"11227-11239","source":"Crossref","is-referenced-by-count":10,"title":["Rethinking Personalized Client Collaboration in Federated Learning"],"prefix":"10.1109","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9235-736X","authenticated-orcid":false,"given":"Leijie","family":"Wu","sequence":"first","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9831-2202","authenticated-orcid":false,"given":"Song","family":"Guo","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9341-412X","authenticated-orcid":false,"given":"Yaohong","family":"Ding","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7263-174X","authenticated-orcid":false,"given":"Junxiao","family":"Wang","sequence":"additional","affiliation":[{"name":"Guangzhou University, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0983-387X","authenticated-orcid":false,"given":"Wenchao","family":"Xu","sequence":"additional","affiliation":[{"name":"The Hong Kong Polytechnic University, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4528-1489","authenticated-orcid":false,"given":"Yufeng","family":"Zhan","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8187-724X","authenticated-orcid":false,"given":"Anne-Marie","family":"Kermarrec","sequence":"additional","affiliation":[{"name":"École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2023.3302410"},{"key":"ref3","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Conf. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref4","article-title":"Personalized federated learning using hypernetworks","author":"Shamsian","year":"2021"},{"key":"ref5","article-title":"Personalized federated learning with first order model optimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhang"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i9.16960"},{"key":"ref7","article-title":"Exploiting shared representations for personalized federated learning","author":"Collins","year":"2021"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i6.16669"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3160699"},{"key":"ref10","first-page":"9","article-title":"Federated computing: Query, learning, and beyond","volume":"46","author":"Tong","year":"2023","journal-title":"IEEE Data Eng. Bull."},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583518"},{"key":"ref12","article-title":"Three approaches for personalization with applications to federated learning","author":"Mansour","year":"2020"},{"key":"ref13","article-title":"Federated evaluation of on-device personalization","author":"Wang","year":"2019"},{"key":"ref14","first-page":"21394","article-title":"Personalized federated learning with moreau envelopes","volume":"33","author":"Dinh","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"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","article-title":"Improving federated learning personalization via model agnostic meta learning","author":"Jiang","year":"2019"},{"key":"ref17","first-page":"15 434","article-title":"Federated multi-task learning under a mixture of distributions","volume":"34","author":"Marfoq","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref18","first-page":"19 586","article-title":"An efficient framework for clustered federated learning","volume":"33","author":"Ghosh","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref19","volume-title":"Game Theory","author":"Fudenberg","year":"1991"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9006179"},{"key":"ref21","article-title":"Interpret federated learning with shapley values","author":"Wang","year":"2019"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9006327"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3375627.3375840"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17093"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599500"},{"key":"ref26","article-title":"Federated learning with non-IID data","author":"Zhao","year":"2018"},{"key":"ref27","article-title":"On the convergence of FedAVG on non-IID data","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li"},{"key":"ref28","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Karimireddy"},{"key":"ref29","article-title":"Values of large games. 6: Evaluating the electoral college exactly","author":"Mann","year":"1962"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2008.04.004"},{"key":"ref31","article-title":"Bounding the estimation error of sampling-based shapley value approximation","author":"Maleki","year":"2013"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"},{"key":"ref33","article-title":"No fear of heterogeneity: Classifier calibration for federated learning with non-IID data","author":"Luo","year":"2021"},{"key":"ref34","article-title":"The MNIST database of handwritten digits","author":"LeCun","year":"1998"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref36","article-title":"Federated learning of deep networks using model averaging","author":"McMahan","year":"2016"},{"key":"ref37","article-title":"Think locally, act globally: Federated learning with local and global representations","author":"Liang","year":"2020"},{"key":"ref38","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017"},{"key":"ref39","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref40","first-page":"12 878","article-title":"Data-free knowledge distillation for heterogeneous federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhu"},{"key":"ref41","article-title":"FedPD: A federated learning framework with optimal rates and adaptivity to non-IID data","author":"Zhang","year":"2020"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00033"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM48880.2022.9796841"},{"key":"ref44","first-page":"5678","article-title":"A survey on gradient inversion: Attacks, defenses and future directions","volume-title":"Proc. 31st Int. Joint Conf. Artif. Intell.","author":"Zhang"}],"container-title":["IEEE Transactions on Mobile Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7755\/10746253\/10517642.pdf?arnumber=10517642","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:12:02Z","timestamp":1732666322000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10517642\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":44,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tmc.2024.3396218","relation":{},"ISSN":["1536-1233","1558-0660","2161-9875"],"issn-type":[{"value":"1536-1233","type":"print"},{"value":"1558-0660","type":"electronic"},{"value":"2161-9875","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12]]}}}