{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T08:45:16Z","timestamp":1771058716104,"version":"3.50.1"},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Huazhu Fu's A*STAR Central Research Fund, and Career Development Fund","award":["C222812010"],"award-info":[{"award-number":["C222812010"]}]},{"name":"A*STAR Advanced Manufacturing and Engineering (AME) Programmatic Fund","award":["A20H4b0141"],"award-info":[{"award-number":["A20H4b0141"]}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"crossref","award":["LQ22F010003"],"award-info":[{"award-number":["LQ22F010003"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Big Data"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1109\/tbdata.2024.3423694","type":"journal-article","created":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T17:20:51Z","timestamp":1720200051000},"page":"648-658","source":"Crossref","is-referenced-by-count":3,"title":["Reliable Federated Disentangling Network for Non-IID Domain Feature"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7882-1747","authenticated-orcid":false,"given":"Meng","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore"}]},{"given":"Kai","family":"Yu","sequence":"additional","affiliation":[{"name":"Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore"}]},{"given":"Chun-Mei","family":"Feng","sequence":"additional","affiliation":[{"name":"Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore"}]},{"given":"Yiming","family":"Qian","sequence":"additional","affiliation":[{"name":"Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2025-9859","authenticated-orcid":false,"given":"Ke","family":"Zou","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, Chengdu, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7088-1411","authenticated-orcid":false,"given":"Lianyu","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9116-1595","authenticated-orcid":false,"given":"Rick Siow Mong","family":"Goh","sequence":"additional","affiliation":[{"name":"Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1449-3072","authenticated-orcid":false,"given":"Xinxing","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore"}]},{"given":"Yong","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9702-5524","authenticated-orcid":false,"given":"Huazhu","family":"Fu","sequence":"additional","affiliation":[{"name":"Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-01585-4"},{"key":"ref2","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"McMahan","year":"2017"},{"key":"ref3","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"Li","year":"2020","journal-title":"Proc. Mach. Learn. Syst."},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"ref5","first-page":"2089","article-title":"Exploiting shared representations for personalized federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Collins","year":"2021"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00987"},{"key":"ref7","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Karimireddy","year":"2020"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3202106"},{"key":"ref9","article-title":"Federated learning with Non-IID data","author":"Zhao","year":"2018"},{"key":"ref10","first-page":"1","article-title":"On the convergence of FedAvg on Non-IID data","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Li","year":"2020"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.5555\/3045390.3045502"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.inffus.2021.05.008","article-title":"A review of uncertainty quantification in deep learning: Techniques, applications and challenges","volume":"76","author":"Abdar","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref13","first-page":"3183","article-title":"Evidential deep learning to quantify classification uncertainty","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Sensoy","year":"2018"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295309"},{"key":"ref15","first-page":"6402","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lakshminarayanan","year":"2017"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-021-01506-3"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1561\/9781680837896"},{"issue":"3","key":"ref18","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MSP.2020.2975749","article-title":"Federated learning: Challenges, methods, and future directions","volume":"37","author":"Li","year":"2020","journal-title":"IEEE Signal Process. Mag."},{"key":"ref19","article-title":"Federated learning based on dynamic regularization","author":"Acar","year":"2021"},{"key":"ref20","article-title":"FedBN: Federated learning on non-iid features via local batch normalization","author":"Li","year":"2021"},{"key":"ref21","article-title":"Federated learning with domain generalization","author":"Zhang","year":"2021"},{"key":"ref22","first-page":"38831","article-title":"FedSR: A simple and effective domain generalization method for federated learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Nguyen","year":"2022"},{"key":"ref23","first-page":"23309","article-title":"PartialFed: Cross-domain personalized federated learning via partial initialization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Sun","year":"2021"},{"key":"ref24","article-title":"beta-VAE: Learning basic visual concepts with a constrained variational framework","author":"Higgins","year":"2017","journal-title":"Proc. Int. Conf. Learn. Representations"},{"key":"ref25","first-page":"3091","article-title":"Learning discrete and continuous factors of data via alternating disentanglement","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Jeong","year":"2019"},{"key":"ref26","first-page":"2615","article-title":"Isolating sources of disentanglement in variational autoencoders","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Chen","year":"2018"},{"key":"ref27","first-page":"9102","article-title":"Invariant representations without adversarial training","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Moyer","year":"2018"},{"key":"ref28","first-page":"2164","article-title":"Learning controllable fair representations","volume-title":"Proc. 22nd Int. Conf. Artif. Intell. Statist.","author":"Song","year":"2019"},{"key":"ref29","article-title":"The information bottleneck method","author":"Tishby","year":"2000"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102516"},{"key":"ref31","first-page":"22479","article-title":"Variational interaction information maximization for cross-domain disentanglement","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hwang","year":"2020"},{"key":"ref32","article-title":"Emerging disentanglement in auto-encoder based unsupervised image content transfer","author":"Press","year":"2020"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00107"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01415"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICME55011.2023.00062"},{"key":"ref36","article-title":"Disentangled federated learning for tackling attributes skew via invariant aggregation and diversity transferring","author":"Luo","year":"2022"},{"key":"ref37","first-page":"1","article-title":"Google photos labeled black people\u2019gorillas\u2019","volume":"1","author":"Guynn","year":"2015","journal-title":"USA Today"},{"key":"ref38","first-page":"16","article-title":"Autonomous vehicles, trust, and driving alternatives: A survey of consumer preferences","volume":"1","author":"Abraham","year":"2016","journal-title":"Proc. Transp. Res. Board 96th Annu. Meeting"},{"key":"ref39","first-page":"9690","article-title":"Uncertainty estimation using a single deep deterministic neural network","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Van Amersfoort","year":"2020"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-42444-7"},{"key":"ref41","first-page":"7482","article-title":"Multi-task learning using uncertainty to weigh losses for scene geometry and semantics","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","author":"Kendall","year":"2018"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3171983"},{"key":"ref43","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","year":"2021"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00985"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-44792-4_3"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1968.tb00722.x"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1515\/9780691214696"},{"key":"ref48","first-page":"1225","article-title":"Interpretation and fusion of hyper opinions in subjective logic","volume-title":"Proc. 15th Int. Conf. Inf. Fusion","author":"J\u00f8sang","year":"2012"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2023.01.019"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6247911"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref52","article-title":"Reading digits in natural images with unsupervised feature learning","volume":"2011","author":"Netzer","year":"2011","journal-title":"Proc. Int. Conf. Neural Inf. Process. Syst. Workshop"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/34.291440"},{"key":"ref54","first-page":"1180","article-title":"Unsupervised domain adaptation by backpropagation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ganin","year":"2015"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00149"}],"container-title":["IEEE Transactions on Big Data"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6687317\/10925345\/10587114.pdf?arnumber=10587114","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T21:17:53Z","timestamp":1742246273000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10587114\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4]]},"references-count":55,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tbdata.2024.3423694","relation":{},"ISSN":["2332-7790","2372-2096"],"issn-type":[{"value":"2332-7790","type":"electronic"},{"value":"2372-2096","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4]]}}}