{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:13:43Z","timestamp":1775326423896,"version":"3.50.1"},"reference-count":118,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176188"],"award-info":[{"award-number":["62176188"]}],"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":["62225113"],"award-info":[{"award-number":["62225113"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research and Development Program of Hubei Province","award":["2021BAA187"],"award-info":[{"award-number":["2021BAA187"]}]},{"name":"Key Research and Development Program of Hubei Province","award":["2022BAD175"],"award-info":[{"award-number":["2022BAD175"]}]},{"name":"Zhejiang lab","award":["2022NF0AB01"],"award-info":[{"award-number":["2022NF0AB01"]}]},{"name":"CCF-Huawei Populus Grove Fund","award":["CCF-HuaweiTC2022003"],"award-info":[{"award-number":["CCF-HuaweiTC2022003"]}]},{"name":"Special Fund of Hubei Luojia Laboratory","award":["220100015"],"award-info":[{"award-number":["220100015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1109\/tpami.2023.3327373","type":"journal-article","created":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T17:53:41Z","timestamp":1698256421000},"page":"712-728","source":"Crossref","is-referenced-by-count":62,"title":["Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learning"],"prefix":"10.1109","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4819-293X","authenticated-orcid":false,"given":"Wenke","family":"Huang","sequence":"first","affiliation":[{"name":"National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, the Hubei Key Laboratory of Multimedia and Network Communication Engineering, the School of Computer Science, Hubei Luojia Laboratory, Wuhan University, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3989-7655","authenticated-orcid":false,"given":"Mang","family":"Ye","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, the Hubei Key Laboratory of Multimedia and Network Communication Engineering, the School of Computer Science, Hubei Luojia Laboratory, Wuhan University, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2050-0486","authenticated-orcid":false,"given":"Zekun","family":"Shi","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, the Hubei Key Laboratory of Multimedia and Network Communication Engineering, the School of Computer Science, Hubei Luojia Laboratory, Wuhan University, Wuhan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0059-8458","authenticated-orcid":false,"given":"Bo","family":"Du","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, the Hubei Key Laboratory of Multimedia and Network Communication Engineering, the School of Computer Science, Hubei Luojia Laboratory, Wuhan University, Wuhan, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00990"},{"key":"ref2","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Krizhevsky"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475455"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-57959-7"},{"key":"ref8","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":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3033286"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3129809"},{"key":"ref12","article-title":"Federated learning for mobile keyboard prediction","author":"Hard","year":"2018"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00107"},{"key":"ref14","first-page":"1","article-title":"Federated learning with diversified preference for humor recognition","volume-title":"Proc. Int. Joint Conf. Artif. Intell. Workshop","author":"Guo"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1561\/9781680837896"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"ref17","article-title":"Heterogeneous federated learning: State-of-the-art and research challenges","volume":"56","author":"Ye","year":"2023","journal-title":"ACM Comput. Sur."},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2022.3160699"},{"key":"ref19","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst.","author":"Li"},{"key":"ref20","first-page":"1","article-title":"Overcoming forgetting in federated learning on non-IID data","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst. Workshop","author":"Shoham"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/9780262170055.001.0001"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2011.06.019"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01099"},{"key":"ref26","volume-title":"Intellectual Property Rights: A Critical History","author":"May","year":"2006"},{"key":"ref27","article-title":"Federated mutual learning","author":"Shen","year":"2020"},{"key":"ref28","article-title":"Specialized federated learning using mixture of experts","author":"Zec","year":"2020"},{"key":"ref29","first-page":"1","article-title":"Think locally, act globally: Federated learning with local and global representations","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst. Workshop","author":"Liang"},{"key":"ref30","first-page":"1","article-title":"FedMD: Heterogenous federated learning via model distillation","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst. Workshop","author":"Li"},{"key":"ref31","article-title":"Cronus: Robust and heterogeneous collaborative learning with black-box knowledge transfer","author":"Chang","year":"2019"},{"key":"ref32","first-page":"2351","article-title":"Ensemble distillation for robust model fusion in federated learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Lin"},{"key":"ref33","first-page":"14 068","article-title":"Group knowledge transfer: Federated learning of large CNNs at the edge","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"He"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00983"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150464"},{"key":"ref36","article-title":"Distilling the knowledge in a neural network","author":"Hinton","year":"2015"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01165"},{"key":"ref38","first-page":"7167","article-title":"A simple unified framework for detecting out-of-distribution samples and adversarial attacks","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Lee"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00276"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/s0079-7421(08)60536-8"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1037\/0033-295x.97.2.285"},{"key":"ref42","article-title":"An empirical investigation of catastrophic forgetting in gradient-based neural networks","author":"Goodfellow","year":"2013"},{"key":"ref43","first-page":"21 394","article-title":"Personalized federated learning with Moreau envelopes","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Dinh"},{"key":"ref44","first-page":"3015","article-title":"Whitening for self-supervised representation learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ermolov"},{"key":"ref45","first-page":"12310","article-title":"Barlow twins: Self-supervised learning via redundancy reduction","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zbontar"},{"key":"ref46","first-page":"8799","article-title":"VICReg: Variance-invariance-covariance regularization for self-supervised learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Bardes"},{"key":"ref47","first-page":"1","article-title":"Contrastive representation distillation","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Tian"},{"key":"ref48","first-page":"5972","article-title":"No fear of heterogeneity: Classifier calibration for federated learning with Non-IID data","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Luo"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00438"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-005-5724-z"},{"key":"ref51","first-page":"1607","article-title":"Born again neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Furlanello"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2016.7472809"},{"key":"ref53","first-page":"6906","article-title":"Does knowledge distillation really work?","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Stanton"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/34.291440"},{"key":"ref56","first-page":"1","article-title":"Reading digits in natural images with unsupervised feature learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst. Workshop","author":"Netzer"},{"key":"ref57","article-title":"Effects of degradations on deep neural network architectures","author":"Roy","year":"2018"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6247911"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15561-1_16"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.572"},{"key":"ref61","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref62","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1145\/3503161.3548764"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/426"},{"key":"ref65","first-page":"1","article-title":"Federated adversarial domain adaptation","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Peng"},{"key":"ref66","article-title":"Communication-efficient federated distillation","author":"Sattler","year":"2020"},{"key":"ref67","article-title":"Model-agnostic round-optimal federated learning via knowledge transfer","author":"Li","year":"2020"},{"key":"ref68","first-page":"12 878","article-title":"Data-free knowledge distillation for heterogeneous federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhu"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-47922-8_8"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_9"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3057446"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.587"},{"key":"ref73","article-title":"Dark experience for general continual learning: A strong, simple baseline","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Buzzega"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2773081"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00581"},{"key":"ref76","article-title":"Progressive neural networks","author":"Rusu","year":"2016"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00303"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00393"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00637"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3013379"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref82","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525087"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.5555\/3495724.3497510"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00650"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i8.16888"},{"key":"ref87","first-page":"1","article-title":"FitNets: Hints for thin deep nets","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Romero"},{"key":"ref88","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i8.16865"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.754"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00409"},{"key":"ref91","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Tan"},{"key":"ref92","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017"},{"key":"ref93","article-title":"WebVision database: Visual learning and understanding from web data","author":"Li","year":"2017"},{"key":"ref94","article-title":"The information bottleneck method","author":"Tishby","year":"2000"},{"key":"ref95","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"ref96","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-021-01453-z"},{"key":"ref97","article-title":"FedBN: Federated learning on non-IID features via local batch normalization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li"},{"key":"ref98","article-title":"Rethinking knowledge distillation via cross-entropy","author":"Yang","year":"2022"},{"key":"ref99","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7503.003.0022"},{"key":"ref100","first-page":"129","article-title":"Learning bounds for domain adaptation","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Blitzer"},{"key":"ref101","first-page":"1041","article-title":"Domain adaptation with multiple sources","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Mansour"},{"key":"ref102","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"},{"key":"ref103","first-page":"8256","article-title":"Algorithms and theory for multiple-source adaptation","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Hoffman"},{"key":"ref104","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781107298019"},{"key":"ref105","article-title":"Federated optimization: Distributed machine learning for on-device intelligence","author":"Kone\u010dn\u1ef3","year":"2016"},{"key":"ref106","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2023.01.019"},{"key":"ref107","first-page":"26 311","article-title":"Federated learning with label distribution skew via logits calibration","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhang"},{"key":"ref108","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01565"},{"key":"ref109","doi-asserted-by":"publisher","DOI":"10.4324\/9781410605337-29"},{"key":"ref110","first-page":"1","article-title":"Federated semi-supervised learning with inter-client consistency & disjoint learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Jeong"},{"key":"ref111","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467254"},{"key":"ref112","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014"},{"key":"ref113","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00947"},{"key":"ref114","first-page":"18661","article-title":"Supervised contrastive learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Khosla"},{"key":"ref115","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1611835114"},{"key":"ref116","first-page":"1","article-title":"Rethinking soft labels for knowledge distillation: A bias-variance tradeoff perspective","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhou"},{"issue":"11","key":"ref117","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":"ref118","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00821"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/10384454\/10295990.pdf?arnumber=10295990","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T19:31:10Z","timestamp":1743795070000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10295990\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2]]},"references-count":118,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2023.3327373","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":[[2024,2]]}}}