{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T06:37:04Z","timestamp":1784875024822,"version":"3.55.0"},"reference-count":115,"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":"National Key R&#x0026;D Program of China","award":["2022YFB2702100"],"award-info":[{"award-number":["2022YFB2702100"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61932004"],"award-info":[{"award-number":["61932004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62225203"],"award-info":[{"award-number":["62225203"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21A20516"],"award-info":[{"award-number":["U21A20516"]}],"id":[{"id":"10.13039\/501100001809","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":[[2024,12]]},"DOI":"10.1109\/tpami.2024.3457751","type":"journal-article","created":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T17:55:32Z","timestamp":1726509332000},"page":"11119-11135","source":"Crossref","is-referenced-by-count":14,"title":["Federated Feature Augmentation and Alignment"],"prefix":"10.1109","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5475-1473","authenticated-orcid":false,"given":"Tianfei","family":"Zhou","sequence":"first","affiliation":[{"name":"Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0247-9866","authenticated-orcid":false,"given":"Ye","family":"Yuan","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9266-4685","authenticated-orcid":false,"given":"Binglu","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ender","family":"Konukoglu","sequence":"additional","affiliation":[{"name":"Computer Vision Lab, ETH Zurich, Z&#x00FC;rich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","article-title":"FedFA: Federated feature augmentation","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhou"},{"key":"ref2","article-title":"Federated learning: Strategies for improving communication efficiency","author":"Kone\u010dn\u1ef3","year":"2016"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1561\/2200000083"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref5","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Conf. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00323-1"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3501296"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3387107"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_17"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2022.3173057"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-11748-0_15"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3195549"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3178128"},{"key":"ref14","article-title":"FedBN: Federated learning on non-iid features via local batch normalization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li"},{"key":"ref15","first-page":"21 554","article-title":"Robust federated learning: The case of affine distribution shifts","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Reisizadeh"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i1.19993"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20050-2_38"},{"key":"ref18","first-page":"18 250","article-title":"Generalized federated learning via sharpness aware minimization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Qu"},{"key":"ref19","first-page":"38 831","article-title":"FedSR: A simple and effective domain generalization method for federated learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Nguyen"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00385"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00044"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00107"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01565"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2023.3304453"},{"key":"ref25","first-page":"32 991","article-title":"Dynamic regularized sharpness aware minimization in federated learning: Approaching global consistency and smooth landscape","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Sun"},{"key":"ref26","article-title":"FedSpeed: Larger local interval, less communication round, and higher generalization accuracy","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Sun"},{"key":"ref27","article-title":"Sharpness-aware minimization for efficiently improving generalization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Foret"},{"key":"ref28","article-title":"FedMix: Approximation of mixup under mean augmented federated learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Yoon"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3387116"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.5555\/3294996.3295163"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20819"},{"key":"ref32","article-title":"Personalized federated learning with feature alignment and classifier collaboration","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Xu"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.167"},{"key":"ref34","article-title":"Domain generalization with mixstyle","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhou"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01220"},{"key":"ref36","article-title":"Deep neural decision trees","author":"Yang","year":"2018"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-55860-377-6.50032-3"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966217"},{"key":"ref39","first-page":"11 814","article-title":"FedScale: Benchmarking model and system performance of federated learning at scale","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Lai"},{"issue":"7","key":"ref40","article-title":"Tiny ImageNet visual recognition challenge","volume":"7","author":"Le","year":"2015","journal-title":"CS 231N"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813687"},{"key":"ref42","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst.","volume":"2","author":"Li"},{"key":"ref43","article-title":"Federated learning based on dynamic regularization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Acar"},{"key":"ref44","first-page":"5132","article-title":"Scaffold: Stochastic controlled averaging for federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Karimireddy"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00987"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00386"},{"key":"ref47","article-title":"Adaptive federated optimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Reddi"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3300886"},{"key":"ref49","article-title":"FedBE: Making Bayesian model ensemble applicable to federated learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Chen"},{"key":"ref50","article-title":"Federated learning with matched averaging","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Wang"},{"key":"ref51","article-title":"Turning the curse of heterogeneity in federated learning into a blessing for out-of-distribution detection","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Yu"},{"key":"ref52","article-title":"Mixup: Beyond empirical risk minimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhang"},{"key":"ref53","first-page":"552","article-title":"Better mixing via deep representations","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Bengio"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref56","article-title":"Visual recognition with deep nearest centroids","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Wang"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00261"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3367952"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00426"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3239194"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-61510-5_12"},{"key":"ref64","article-title":"Regularization for deep learning: A taxonomy","author":"Kuka\u010dka","year":"2017"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00612"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01558"},{"key":"ref67","first-page":"6438","article-title":"Manifold mixup: Better representations by interpolating hidden states","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Verma"},{"key":"ref68","article-title":"Uncertainty modeling for out-of-distribution generalization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.02019"},{"key":"ref70","first-page":"10","article-title":"Domain generalization via invariant feature representation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Muandet"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00787"},{"key":"ref73","first-page":"2178","article-title":"Generalizing from several related classification tasks to a new unlabeled sample","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Blanchard"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00566"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.591"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00876"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59713-9_46"},{"key":"ref78","first-page":"1006","article-title":"MetaReg: Towards domain generalization using meta-regularization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Balaji"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11596"},{"key":"ref80","first-page":"6450","article-title":"Domain generalization via model-agnostic learning of semantic features","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Dou"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00438"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3271851"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467309"},{"key":"ref84","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":"ref85","article-title":"What do we mean by generalization in federated learning?","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Yuan"},{"key":"ref86","first-page":"395","article-title":"Vicinal risk minimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Chapelle"},{"key":"ref87","first-page":"6256","article-title":"Unsupervised data augmentation for consistency training","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Xie"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.2307\/2331554"},{"issue":"11","key":"ref89","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref90","article-title":"Auto-encoding variational bayes","author":"Kingma","year":"2013"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1995.7.1.108"},{"key":"ref92","first-page":"16 603","article-title":"Explicit regularisation in Gaussian noise injections","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Camuto"},{"key":"ref93","article-title":"Noisy feature mixup","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Lim"},{"key":"ref94","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00044"},{"key":"ref95","first-page":"1310","article-title":"Certified adversarial robustness via randomized smoothing","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Cohen"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6247911"},{"key":"ref97","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00149"},{"key":"ref98","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2974574"},{"key":"ref99","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref100","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"ref101","article-title":"Caltech-256 object category dataset","author":"Griffin","year":"2007"},{"key":"ref102","article-title":"Measuring the effects of non-identical data distribution for federated visual classification","author":"Hsu","year":"2019"},{"key":"ref103","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref104","first-page":"11 058","article-title":"Multi-level branched regularization for federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Kim"},{"key":"ref105","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01167"},{"key":"ref106","article-title":"Towards understanding and mitigating dimensional collapse in heterogeneous federated learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Shi"},{"key":"ref107","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01133"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref110","first-page":"9929","article-title":"Understanding contrastive representation learning through alignment and uniformity on the hypersphere","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Wang"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"ref112","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.102198"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00065"},{"key":"ref114","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-63076-8_2"},{"key":"ref115","article-title":"iDLG: Improved deep leakage from gradients","author":"Zhao","year":"2020"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/34\/10746266\/10680999.pdf?arnumber=10680999","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T04:12:43Z","timestamp":1743826363000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10680999\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12]]},"references-count":115,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2024.3457751","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":[[2024,12]]}}}