{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T16:11:46Z","timestamp":1778947906527,"version":"3.51.4"},"reference-count":63,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":["62472089"],"award-info":[{"award-number":["62472089"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans.Inform.Forensic Secur."],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tifs.2025.3602228","type":"journal-article","created":{"date-parts":[[2025,8,25]],"date-time":"2025-08-25T20:43:50Z","timestamp":1756154630000},"page":"9328-9342","source":"Crossref","is-referenced-by-count":2,"title":["Principal Angle-Based Clustered Federated Learning With Local Differential Privacy for Heterogeneous Data"],"prefix":"10.1109","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9530-4160","authenticated-orcid":false,"given":"Ruyu","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2534-4750","authenticated-orcid":false,"given":"Weiwei","family":"Ni","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nan","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8328-5878","authenticated-orcid":false,"given":"Lihe","family":"Hou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongyue","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Southeast University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2021.3075439"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3570953"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.3015958"},{"key":"ref4","article-title":"Privacy preserving stochastic channel-based federated learning with neural network pruning","author":"Shao","year":"2019","journal-title":"arXiv:1910.02115"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/PerComWorkshops56833.2023.10150285"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/tit.2022.3192506"},{"key":"ref7","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. 3rd Mach. Learn. Syst. Conf.","author":"Li"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.07.098"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00988"},{"key":"ref11","first-page":"32991","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":"ref12","first-page":"19","article-title":"Oort: Efficient federated learning via guided participant selection","volume-title":"Proc. 15th USENIX Symp. Operat. Syst. Design Implement. (OSDI)","author":"Lai"},{"key":"ref13","article-title":"Federated learning based on dynamic regularization","author":"Acar","year":"2021","journal-title":"arXiv:2111.04263"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02352"},{"key":"ref15","article-title":"Client selection in federated learning: Convergence analysis and power-of-choice selection strategies","author":"Cho","year":"2020","journal-title":"arXiv:2010.01243"},{"key":"ref16","first-page":"6357","article-title":"Ditto: Fair and robust federated learning through personalization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Li"},{"key":"ref17","first-page":"3557","article-title":"Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach","volume-title":"Proc. NeurIPS Conf.","volume":"33","author":"Fallah"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00535"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.sysarc.2022.102754"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539384"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3299947"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3152581"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.12"},{"key":"ref24","article-title":"FLASHE: Additively symmetric homomorphic encryption for cross-silo federated learning","author":"Jiang","year":"2021","journal-title":"arXiv:2109.00675"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2024.3392424"},{"key":"ref26","first-page":"493","article-title":"BatchCrypt: Efficient homomorphic encryption for cross-silo federated learning","volume-title":"Proc. USENIX Annu. Tech. Conf.","author":"Zhang"},{"key":"ref27","article-title":"Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption","author":"Hardy","year":"2017","journal-title":"arXiv:1711.10677"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9413764"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3411402"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3378679.3394533"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP51992.2021.00029"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT44484.2020.9174426"},{"key":"ref33","article-title":"LDP-FL: Practical private aggregation in federated learning with local differential privacy","author":"Sun","year":"2020","journal-title":"arXiv:2007.15789"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/3564625.3567973"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2025.3533090"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2022.3166386"},{"key":"ref37","article-title":"Three approaches for personalization with applications to federated learning","author":"Mansour","year":"2020","journal-title":"arXiv:2002.10619"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-022-01046-x"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207469"},{"key":"ref40","article-title":"FedGroup: Efficient clustered federated learning via decomposed data-driven measure","author":"Duan","year":"2020","journal-title":"arXiv:2010.06870"},{"key":"ref41","first-page":"15434","article-title":"Federated multi-task learning under a mixture of distributions","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Marfoq"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i7.20785"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i8.26197"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/3625558"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/BF01890115"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2013.53"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/11681878_14"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1561\/9781601988195"},{"issue":"1","key":"ref49","first-page":"3129","article-title":"Large-scale SVD and manifold learning","volume":"14","author":"Talwalkar","year":"2013","journal-title":"J. Mach. Learn. Res."},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390204"},{"key":"ref51","article-title":"Subspace alignment for domain adaptation","author":"Fernando","year":"2014","journal-title":"arXiv:1409.5241"},{"key":"ref52","article-title":"On the convergence of local descent methods in federated learning","author":"Haddadpour","year":"2019","journal-title":"arXiv:1910.14425"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46128-1_50"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2012.2211477"},{"key":"ref55","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017","journal-title":"arXiv:1708.07747"},{"key":"ref56","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref57","first-page":"4","article-title":"Reading digits in natural images with unsupervised feature learning","volume-title":"Proc. NIPS Workshop","author":"Netzer"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00840"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.4.541"},{"key":"ref60","article-title":"UMAP: Uniform manifold approximation and projection for dimension reduction","author":"McInnes","year":"2018","journal-title":"arXiv:1802.03426"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00065"},{"key":"ref62","first-page":"16937","article-title":"Inverting gradients-how easy is it to break privacy in federated learning?","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Geiping"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00068"}],"container-title":["IEEE Transactions on Information Forensics and Security"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/10206\/10810755\/11137410.pdf?arnumber=11137410","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T05:07:01Z","timestamp":1757567221000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11137410\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":63,"URL":"https:\/\/doi.org\/10.1109\/tifs.2025.3602228","relation":{},"ISSN":["1556-6013","1556-6021"],"issn-type":[{"value":"1556-6013","type":"print"},{"value":"1556-6021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}