{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T16:07:21Z","timestamp":1784131641625,"version":"3.55.0"},"reference-count":53,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":["IEEE Trans. on Mobile Comput."],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/tmc.2022.3230712","type":"journal-article","created":{"date-parts":[[2022,12,20]],"date-time":"2022-12-20T18:49:57Z","timestamp":1671562197000},"page":"1-17","source":"Crossref","is-referenced-by-count":15,"title":["Like Attracts Like: Personalized Federated Learning in Decentralized Edge Computing"],"prefix":"10.1109","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7660-735X","authenticated-orcid":false,"given":"Zhenguo","family":"Ma","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0839-3892","authenticated-orcid":false,"given":"Yang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3831-4577","authenticated-orcid":false,"given":"Hongli","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1764-9303","authenticated-orcid":false,"given":"Jianchun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2979-7151","authenticated-orcid":false,"given":"Yinxing","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2016.2579198"},{"key":"ref2","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3381006"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3307334.3328589"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488756"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2021.108429"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00039"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2022.3192506"},{"key":"ref9","first-page":"864","article-title":"Fully decentralized joint learning of personalized models and collaboration graphs","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Zantedeschi"},{"key":"ref10","article-title":"LotteryFL: Personalized and communication-efficient federated learning with lottery ticket hypothesis on non-iid datasets","author":"Li","year":"2020"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i9.16960"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2904348"},{"key":"ref13","article-title":"A field guide to federated optimization","author":"Wang","year":"2021"},{"key":"ref14","first-page":"451","article-title":"MPI communication performance in a heterogeneous environment with raspberry pi","volume-title":"Proc. Adv. Parallel Distrib. Process., Appl.","author":"Valderrama Riveros"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.5555\/2685048.2685095"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2022.3216326"},{"key":"ref17","article-title":"Personalized federated learning with clustered generalization","author":"Tang","year":"2021"},{"key":"ref18","first-page":"21394","article-title":"Personalized federated learning with moreau envelopes","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Dinh"},{"key":"ref19","article-title":"The lottery ticket hypothesis: Finding sparse, trainable neural networks","author":"Frankle"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICAICA50127.2020.9182705"},{"key":"ref21","article-title":"Decentralized federated learning of deep neural networks on non-iid data","author":"Onoszko","year":"2021"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01104"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9787.1982.tb00758.x"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/8996.003.0006"},{"key":"ref25","first-page":"344","article-title":"Stochastic gradient push for distributed deep learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Assran"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1137\/0314056"},{"key":"ref27","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/JSAIT.2020.2985917"},{"key":"ref29","first-page":"17 749","article-title":"The implicit bias of minima stability: A view from function space","volume":"34","author":"Mulayoff","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2022.3186936"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00062"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3447993.3483278"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/0024-3795(94)00344-D"},{"key":"ref34","article-title":"Incremental methods for weakly convex optimization","author":"Li","year":"2019"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2016.7422408"},{"key":"ref36","article-title":"Exploiting shared representations for personalized federated learning","author":"Collins","year":"2021"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/s11036-020-01586-4"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.5555\/3454287.3455008"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1515\/9781400841356.38"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2009.2026270"},{"key":"ref41","first-page":"8","article-title":"CCTorus: A new torus topology for interconnection networks","volume-title":"Proc. Int. Conf. Adv. Comput. Technol. Creative Media","author":"Yadav"},{"key":"ref42","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966217"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-48308-5_54"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.5555\/2999134.2999257"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3075291"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/23.212330"},{"key":"ref49","first-page":"3557","article-title":"Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach","volume":"33","author":"Fallah","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ICTC52510.2021.9620852"},{"key":"ref51","article-title":"Adaptive personalized federated learning","author":"Deng","year":"2020"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/SPAWC51858.2021.9593126"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCSW53096.2021.00012"}],"container-title":["IEEE Transactions on Mobile Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7755\/4358975\/09993756.pdf?arnumber=9993756","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T02:05:50Z","timestamp":1705025150000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9993756\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":53,"URL":"https:\/\/doi.org\/10.1109\/tmc.2022.3230712","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":[[2022]]}}}