{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T21:00:07Z","timestamp":1774558807473,"version":"3.50.1"},"reference-count":47,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Bosch-Fudan Large Scale Collaboration and in part by the Energy-Efficient project funded by the Federal Ministry for Economic Affairs and Climate Action"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Automat. Sci. Eng."],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/tase.2025.3547220","type":"journal-article","created":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T13:44:00Z","timestamp":1741009440000},"page":"3388-3398","source":"Crossref","is-referenced-by-count":0,"title":["FeDMus: Federated Dynamic Example Mining for Unlabeled Sensor Data in Industrial Automation"],"prefix":"10.1109","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6048-0868","authenticated-orcid":false,"given":"Wanlin","family":"Yang","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Fudan University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8414-2601","authenticated-orcid":false,"given":"Haowei","family":"Chen","sequence":"additional","affiliation":[{"name":"Fu Foundation School of Engineering and Applied Science, Columbia University, New York, NY, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9415-8997","authenticated-orcid":false,"given":"Tobias","family":"Schlagenhauf","sequence":"additional","affiliation":[{"name":"Research Portfolio of AI in Production, Bosch Corporate Research, Renningen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3087-4636","authenticated-orcid":false,"given":"Zhenzhen","family":"Li","sequence":"additional","affiliation":[{"name":"Research Portfolio of AI in Production, Bosch Corporate Research, Renningen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8546-1329","authenticated-orcid":false,"given":"Zhuo","family":"Zou","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Fudan University, Shanghai, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.measen.2023.100944"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2023.3280337"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2021.108105"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2019.106587"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3390\/s23031305"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-022-03344-3"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ETFA.2016.7733659"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3030072"},{"key":"ref9","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-012-0507-8"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.02036"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00982"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3227563"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/SMC53654.2022.9945452"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01087"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2023.3320147"},{"issue":"11","key":"ref17","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00384"},{"key":"ref19","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst.","volume":"2","author":"Li"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3160699"},{"key":"ref21","article-title":"A survey on heterogeneous federated learning","author":"Gao","year":"2022","journal-title":"arXiv:2210.04505"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.07.098"},{"key":"ref23","article-title":"Divergence-aware federated self-supervised learning","author":"Zhuang","year":"2022","journal-title":"arXiv:2204.04385"},{"key":"ref24","article-title":"Mocosfl: Enabling cross-client collaborative self-supervised learning","volume-title":"Proc. 11th Int. Conf. Learn. Represent.","author":"Li"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00993"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107671"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2024.121012"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2014.6889457"},{"key":"ref29","first-page":"1183","article-title":"Deep Bayesian active learning with image data","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Gal"},{"key":"ref30","article-title":"Deep active learning over the long tail","author":"Geifman","year":"2017","journal-title":"arXiv:1711.00941"},{"key":"ref31","article-title":"Deep batch active learning by diverse, uncertain gradient lower bounds","author":"Ash","year":"2019","journal-title":"arXiv:1906.03671"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TSM.2020.2974867"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58517-4_9"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00917"},{"key":"ref35","first-page":"29074","article-title":"Can multi-label classification networks know what they don\u2019t know?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Wang"},{"key":"ref36","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Lakshminarayanan"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00176"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01000"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3376746"},{"key":"ref40","volume-title":"Callbacks-Keras Documentation","author":"Developers","year":"2024"},{"key":"ref41","volume-title":"Foundations of Machine Learning","author":"Mohri","year":"2018"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.procir.2022.04.022"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.36001\/phme.2016.v3i1.1577"},{"key":"ref44","first-page":"3","article-title":"A public domain dataset for human activity recognition using smartphones","volume-title":"Proc. Esann","volume":"3","author":"Anguita"},{"key":"ref45","article-title":"Character trajectories","author":"Williams","journal-title":"UCI Machine Learning Repository"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymssp.2023.111068"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7966039"}],"container-title":["IEEE Transactions on Automation Science and Engineering"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/8856\/11323516\/10908904.pdf?arnumber=10908904","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T19:53:27Z","timestamp":1774554807000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10908904\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":47,"URL":"https:\/\/doi.org\/10.1109\/tase.2025.3547220","relation":{},"ISSN":["1545-5955","1558-3783"],"issn-type":[{"value":"1545-5955","type":"print"},{"value":"1558-3783","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}