{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T17:30:18Z","timestamp":1776879018176,"version":"3.51.2"},"reference-count":44,"publisher":"Tsinghua University Press","issue":"4","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072485"],"award-info":[{"award-number":["62072485"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Big Data Min. Anal."],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.26599\/bdma.2024.9020065","type":"journal-article","created":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T19:23:58Z","timestamp":1733340238000},"page":"1050-1064","source":"Crossref","is-referenced-by-count":11,"title":["A Remedy for Heterogeneous Data: Clustered Federated Learning with Gradient Trajectory"],"prefix":"10.26599","volume":"7","author":[{"given":"Ruiqi","family":"Liu","sequence":"first","affiliation":[{"name":"School of Intelligent Systems Engineering, Sun Yat-Sen University,Shenzhen,China,510275"}]},{"given":"Songcan","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Intelligent Systems Engineering, Sun Yat-Sen University,Shenzhen,China,510275"}]},{"given":"Linsi","family":"Lan","sequence":"additional","affiliation":[{"name":"School of Intelligent Systems Engineering, Sun Yat-Sen University,Shenzhen,China,510275"}]},{"given":"Junbo","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Intelligent Systems Engineering, Sun Yat-Sen University,Shenzhen,China,510275"}]},{"given":"Krishna","family":"Kant","sequence":"additional","affiliation":[{"name":"Temple University,Computer and Information Systems Department,Philadelphia,AZ,USA,19122"}]},{"given":"Neville","family":"Calleja","sequence":"additional","affiliation":[{"name":"University of Malta,Department of Policy in Health,Msida,MSD,Malta,2080"}]}],"member":"11138","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2016.2558446"},{"key":"ref2","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2016","journal-title":"arXiv preprint"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3286490.3286559"},{"key":"ref6","article-title":"On the convergence of FedAvg on non-IID data","author":"Li","year":"2019","journal-title":"arXiv preprint"},{"key":"ref7","article-title":"On data efficiency of meta-learning","volume-title":"Proc. Int. Conf. on Artificial Intelligence and Statistics","author":"Al-Shedivat","year":"2021"},{"key":"ref8","first-page":"3557","article-title":"Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach","volume-title":"Proc. of the 34th Int. Conf. on Neural Information Processing Systems","author":"Fallah"},{"key":"ref9","article-title":"Convergence and accuracy trade-offs in federated learning and meta-learning","volume-title":"Proc. Int. Conf. on Artificial Intelligence and Statistics","author":"Charles","year":"2021"},{"key":"ref10","article-title":"Three approaches for personalization with applications to federated learning","author":"Mansour","year":"2020","journal-title":"arXiv preprint"},{"key":"ref11","article-title":"Ditto: Fair and robust federated learning through personalization","volume-title":"Proc. Int. Conf. on Machine Learning, arXiv preprint","author":"Li","year":"2021"},{"key":"ref12","first-page":"23309","article-title":"PartialFed: Cross-domain personalized federated learning via partial initialization","volume-title":"Proc. of the 35th Int. Conf. on Neural Information Processing Systems","author":"Sun","year":"2021"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/WorldS450073.2020.9210355"},{"key":"ref14","article-title":"No fear of heterogeneity: Classifier calibration for federated learning with non-IID data","author":"Luo","year":"2021","journal-title":"arXiv preprint"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467254"},{"key":"ref16","article-title":"Exploiting shared representations for personalized federated learning","author":"Collins","year":"2021","journal-title":"arXiv preprint"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref18","article-title":"How transferable are features in deep neural networks?","author":"Yosinski","year":"2014","journal-title":"arXiv preprint"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3015958"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00042"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207469"},{"key":"ref22","first-page":"14774","article-title":"Deep leakage from gradients","volume-title":"Proc. 33rd Int. Joint Conf. Neural Information Processing Systems, Networks","author":"Zhu"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00029"},{"key":"ref24","article-title":"An efficient framework for clustered federated learning","author":"Ghosh","year":"2020","journal-title":"arXiv preprint"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2021.100470"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.47"},{"key":"ref27","article-title":"Prototypical networks for few-shot learning","author":"Snell","year":"2017","journal-title":"arXiv preprint"},{"key":"ref28","article-title":"Classification is a strong baseline for deep metric learning","author":"Zhai","year":"2018","journal-title":"arXiv preprint"},{"key":"ref29","article-title":"User-level label leakage from gradients in federated learning","author":"Wainakh","year":"2021","journal-title":"arXiv preprint"},{"key":"ref30","article-title":"Inverting Gradients: How easy is it to break privacy in federated learning?","author":"Geiping","year":"2020","journal-title":"arXiv preprint"},{"key":"ref31","article-title":"iDLG: Improved deep leakage from gradients","author":"Zhao","year":"2020","journal-title":"arXiv preprint"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-80328-4"},{"key":"ref33","article-title":"FedBN: Federated learning on non-IID features via local batch normalization","author":"Li","year":"2021","journal-title":"arXiv preprint"},{"key":"ref34","article-title":"Not all samples are created equal: Deep learning with importance sampling","volume-title":"Proc. of Int. Conf. on machine learning","author":"Katharopoulos","year":"2018"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2020.3015268"},{"key":"ref36","volume-title":"Affinity propagation: Clustering data bypassing messages","author":"Dueck","year":"2009"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/fuzzy.2004.1375706"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.10.007"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134012"},{"key":"ref40","article-title":"FedMD: Heterogenous federated learning via model distillation","author":"Li","year":"2019","journal-title":"arXiv preprint"},{"key":"ref41","article-title":"Federated learning with only positive labels","volume-title":"Proc. of Int. Conf. on Machine Learning","author":"Yu","year":"2020"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1002\/widm.53"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1452"}],"container-title":["Big Data Mining and Analytics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/8254253\/10778131\/10778109.pdf?arnumber=10778109","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T06:07:04Z","timestamp":1733378824000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10778109\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":44,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.26599\/bdma.2024.9020065","relation":{},"ISSN":["2096-0654","2097-406X"],"issn-type":[{"value":"2096-0654","type":"print"},{"value":"2097-406X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12]]}}}