{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T20:47:31Z","timestamp":1774039651729,"version":"3.50.1"},"reference-count":61,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,5,1]],"date-time":"2024-05-01T00:00:00Z","timestamp":1714521600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Ministry of Education Academic Research Fund","award":["A-8000423-00-00"],"award-info":[{"award-number":["A-8000423-00-00"]}]},{"name":"AcRF Tier 1","award":["A-8000980-00-00"],"award-info":[{"award-number":["A-8000980-00-00"]}]},{"name":"AcRF Tier 1","award":["A-8000189-01-00"],"award-info":[{"award-number":["A-8000189-01-00"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276256"],"award-info":[{"award-number":["62276256"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005090","name":"Beijing Nova Program","doi-asserted-by":"publisher","award":["Z211100002121108"],"award-info":[{"award-number":["Z211100002121108"]}],"id":[{"id":"10.13039\/501100005090","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1109\/tpami.2023.3338063","type":"journal-article","created":{"date-parts":[[2023,11,30]],"date-time":"2023-11-30T19:05:56Z","timestamp":1701371156000},"page":"2936-2949","source":"Crossref","is-referenced-by-count":18,"title":["Understanding and Mitigating Dimensional Collapse in Federated Learning"],"prefix":"10.1109","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7594-7616","authenticated-orcid":false,"given":"Yujun","family":"Shi","sequence":"first","affiliation":[{"name":"National University of Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3890-1894","authenticated-orcid":false,"given":"Jian","family":"Liang","sequence":"additional","affiliation":[{"name":"CRIPAC and MAIS, Institute of Automation, Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3479-282X","authenticated-orcid":false,"given":"Wenqing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Bytedance Inc., Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3562-3094","authenticated-orcid":false,"given":"Chuhui","family":"Xue","sequence":"additional","affiliation":[{"name":"Bytedance Inc., Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5008-4527","authenticated-orcid":false,"given":"Vincent Y. F.","family":"Tan","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2570-9118","authenticated-orcid":false,"given":"Song","family":"Bai","sequence":"additional","affiliation":[{"name":"Bytedance Inc., Singapore"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref2","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst.","volume":"2","author":"Li"},{"key":"ref3","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Karimireddy"},{"key":"ref4","first-page":"7611","article-title":"Tackling the objective inconsistency problem in heterogeneous federated optimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/icde53745.2022.00077"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"ref7","article-title":"Federated learning based on dynamic regularization","author":"Acar","year":"2021"},{"key":"ref8","article-title":"Federated learning via posterior averaging: A new perspective and practical algorithms","author":"Al-Shedivat","year":"2020"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i11.17219"},{"key":"ref10","first-page":"18250","article-title":"Generalized federated learning via sharpness aware minimization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Qu"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20050-2_38"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00821"},{"key":"ref13","article-title":"Measuring the effects of non-identical data distribution for federated visual classification","author":"Hsu","year":"2019"},{"key":"ref14","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"},{"key":"ref15","article-title":"Federated learning with matched averaging","author":"Wang","year":"2020"},{"key":"ref16","first-page":"2351","article-title":"Ensemble distillation for robust model fusion in federated learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lin"},{"key":"ref17","article-title":"Adaptive federated optimization","author":"Reddi","year":"2020"},{"key":"ref18","article-title":"FedExP: Speeding up federated averaging via extrapolation","author":"Jhunjhunwala","year":"2023"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467254"},{"key":"ref20","first-page":"26311","article-title":"Federated learning with label distribution skew via logits calibration","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhang"},{"key":"ref21","article-title":"Federated learning with personalization layers","author":"Arivazhagan","year":"2019"},{"key":"ref22","first-page":"6357","article-title":"Ditto: Fair and robust federated learning through personalization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li"},{"key":"ref23","first-page":"3557","article-title":"Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Fallah"},{"key":"ref24","first-page":"21394","article-title":"Personalized federated learning with moreau envelopes","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Dinh"},{"key":"ref25","first-page":"2304","article-title":"Lower bounds and optimal algorithms for personalized federated learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hanzely"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i9.16960"},{"key":"ref27","article-title":"Personalized federated learning with first order model optimization","author":"Zhang","year":"2020"},{"key":"ref28","article-title":"FedBN: Federated learning on non-iid features via local batch normalization","author":"Li","year":"2021"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2024.3352628"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/tsp.2022.3198176"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.14778\/3547305.3547316"},{"key":"ref32","first-page":"2738","article-title":"Compressed-VFL: Communication-efficient learning with vertically partitioned data","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Castiglia"},{"key":"ref33","first-page":"22045","article-title":"Model fusion via optimal transport","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Singh"},{"key":"ref34","first-page":"13857","article-title":"Deep neural network fusion via graph matching with applications to model ensemble and federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Liu"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/3377930.3390197"},{"key":"ref36","first-page":"23965","article-title":"Model soups: Averaging weights of multiple fine-tuned models improves accuracy without increasing inference time","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wortsman"},{"key":"ref37","article-title":"Git re-basin: Merging models modulo permutation symmetries","author":"Ainsworth","year":"2022"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2015.7280769"},{"key":"ref39","first-page":"8242","article-title":"Revisiting training strategies and generalization performance in deep metric learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Roth"},{"key":"ref40","article-title":"Understanding dimensional collapse in contrastive self-supervised learning","author":"Jing","year":"2021"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00946"},{"key":"ref42","first-page":"3015","article-title":"Whitening for self-supervised representation learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ermolov"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01622"},{"key":"ref44","first-page":"244","article-title":"On the optimization of deep networks: Implicit acceleration by overparameterization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Arora"},{"key":"ref45","article-title":"Implicit regularization in deep matrix factorization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Arora"},{"key":"ref46","first-page":"10268","article-title":"Understanding self-supervised learning dynamics without contrastive pairs","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Tian"},{"key":"ref47","article-title":"VICReg: Variance-invariance-covariance regularization for self-supervised learning","author":"Bardes","year":"2021"},{"key":"ref48","first-page":"12310","article-title":"Barlow twins: Self-supervised learning via redundancy reduction","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zbontar"},{"key":"ref49","article-title":"Reducing overfitting in deep networks by decorrelating representations","author":"Cogswell","year":"2015"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00089"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0063"},{"key":"ref52","first-page":"7252","article-title":"Bayesian nonparametric federated learning of neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yurochkin"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref54","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"issue":"7","key":"ref55","first-page":"3","article-title":"Tiny ImageNet visual recognition challenge","volume":"7","author":"Le","year":"2015","journal-title":"CS 231N"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6247911"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00149"},{"key":"ref58","article-title":"AutoAugment: Learning augmentation policies from data","author":"Cubuk","year":"2018"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.5555\/3045118.3045167"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/10490207\/10336535.pdf?arnumber=10336535","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T19:39:20Z","timestamp":1743795560000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10336535\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5]]},"references-count":61,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2023.3338063","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5]]}}}