{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T17:41:58Z","timestamp":1772818918624,"version":"3.50.1"},"reference-count":57,"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":[{"DOI":"10.13039\/501100001475","name":"Nanyang Technological University","doi-asserted-by":"publisher","award":["020724-00001"],"award-info":[{"award-number":["020724-00001"]}],"id":[{"id":"10.13039\/501100001475","id-type":"DOI","asserted-by":"publisher"}]},{"name":"RIE2025 Industry Alignment Fund"},{"name":"Industry Collaboration Projects","award":["I2301E0026"],"award-info":[{"award-number":["I2301E0026"]}]},{"DOI":"10.13039\/501100001348","name":"Agency for Science, Technology and Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001348","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Alibaba Group"},{"DOI":"10.13039\/501100001475","name":"Nanyang Technological University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001475","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001381","name":"National Research Foundation Singapore","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001381","id-type":"DOI","asserted-by":"publisher"}]},{"name":"DSO National Laboratories"},{"name":"AI Singapore Programme AISG","award":["AISG2-RP-2020-019"],"award-info":[{"award-number":["AISG2-RP-2020-019"]}]},{"name":"National Key R&D Program of China","award":["2021YFB2900103"],"award-info":[{"award-number":["2021YFB2900103"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61932016"],"award-info":[{"award-number":["61932016"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The Fundamental Research Funds for the Central Universities","award":["WK2150110024"],"award-info":[{"award-number":["WK2150110024"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Mobile Comput."],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1109\/tmc.2024.3455331","type":"journal-article","created":{"date-parts":[[2024,9,6]],"date-time":"2024-09-06T18:39:48Z","timestamp":1725647988000},"page":"15196-15209","source":"Crossref","is-referenced-by-count":3,"title":["Joint Client-and-Sample Selection for Federated Learning via Bi-Level Optimization"],"prefix":"10.1109","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3592-4153","authenticated-orcid":false,"given":"Anran","family":"Li","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics and Data Science, School of Medicine, Yale University, New Haven, CT, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9353-9042","authenticated-orcid":false,"given":"Guangjing","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of South Florida, Tampa, FL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5058-4660","authenticated-orcid":false,"given":"Ming","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computing and Information Systems, Singapore Management University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6944-8378","authenticated-orcid":false,"given":"Jianfei","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computing and Information Systems, Singapore Management University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1004-8588","authenticated-orcid":false,"given":"Lan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6062-207X","authenticated-orcid":false,"given":"Luu Anh","family":"Tuan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6893-8650","authenticated-orcid":false,"given":"Han","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University, Singapore"}],"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. Int. Conf. Mach. Learn.","author":"McMahan"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3137321"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2023.3264786"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/RTSS59052.2023.00022"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE55515.2023.00120"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/BigCom57025.2022.00051"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-021-01506-3"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488723"},{"key":"ref9","first-page":"2525","article-title":"Not all samples are created equal: Deep learning with importance sampling","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Katharopoulos"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOM56429.2023.10099110"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM53939.2023.10228895"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00039"},{"key":"ref13","first-page":"19","article-title":"Oort: Efficient federated learning via guided participant selection","volume-title":"Proc. 15th USENIX Symp. Operating Syst. Des. Implementation","author":"Lai"},{"key":"ref14","article-title":"Client selection in federated learning: Convergence analysis and power-of-choice selection strategies","author":"Cho","year":"2020"},{"key":"ref15","article-title":"Sample selection with deadline control for efficient federated learning on heterogeneous clients","author":"Shin","year":"2022"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i11.21505"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00073"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155494"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.52591\/lxai2020071310"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i7.20755"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3617332"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553380"},{"key":"ref23","first-page":"1311","article-title":"Automated curriculum learning for neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Graves"},{"key":"ref24","first-page":"872","article-title":"What is the effect of importance weighting in deep learning?","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Byrd"},{"key":"ref25","article-title":"Variance reduction in SGD by distributed importance sampling","author":"Alain","year":"2015"},{"key":"ref26","first-page":"4334","article-title":"Learning to reweight examples for robust deep learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ren"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3125459"},{"key":"ref28","first-page":"2304","article-title":"MentorNet: Learning data-driven curriculum for very deep neural networks on corrupted labels","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Jiang"},{"key":"ref29","first-page":"8536","article-title":"Co-teaching: Robust training of deep neural networks with extremely noisy labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Han"},{"key":"ref30","first-page":"1","article-title":"Diverse client selection for federated learning via submodular maximization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Balakrishnan"},{"key":"ref31","article-title":"Data selection for federated learning with relevant and irrelevant data at clients","author":"Tuor","year":"2020"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412599"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2023.3333879"},{"key":"ref34","first-page":"1540","article-title":"Optimizing millions of hyperparameters by implicit differentiation","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Lorraine"},{"key":"ref35","first-page":"1885","article-title":"Understanding black-box predictions via influence functions","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Koh"},{"key":"ref36","article-title":"FedNest: Federated bilevel, minimax, and compositional optimization","author":"Tarzanagh","year":"2022"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.14778\/3538598.3538614"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.1980.10486199"},{"key":"ref39","first-page":"5255","article-title":"On the accuracy of influence functions for measuring group effects","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Koh"},{"key":"ref40","article-title":"Pytorch","year":"2022"},{"key":"ref41","article-title":"The MNIST database of handwritten digits","author":"LeCun","year":"1998"},{"key":"ref42","article-title":"LEAF: A benchmark for federated settings","author":"Caldas","year":"2018"},{"key":"ref43","article-title":"LEAF: A benchmark for federated settings","author":"Sebastian Caldas"},{"key":"ref44","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009","journal-title":"Semanticscholar"},{"key":"ref45","article-title":"Cifar-100 dataset"},{"key":"ref46","article-title":"Kdd cup 1999 data data set","author":"Archive"},{"key":"ref47","article-title":"Ag\u2019s corpus of news articles","author":"Gulli","year":"2004"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref49","article-title":"Multi layer perceptron","volume":"24","author":"Riedmiller","year":"2014","journal-title":"Mach. Learn. Lab Special Lecture"},{"key":"ref50","article-title":"Sentiment analysis","author":"Trevett","year":"2017"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_8"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.fl4nlp-1.1"},{"key":"ref54","article-title":"Differentially private federated learning: A client level perspective","author":"Geyer","year":"2017"},{"key":"ref55","article-title":"Asynchronous federated optimization","author":"Xie","year":"2019"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1561\/0400000042"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3517836"}],"container-title":["IEEE Transactions on Mobile Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/7755\/10746253\/10668811.pdf?arnumber=10668811","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:34:29Z","timestamp":1732667669000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10668811\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":57,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tmc.2024.3455331","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]]}}}