{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T18:58:12Z","timestamp":1763060292618,"version":"3.45.0"},"reference-count":48,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"National Key R&D Program of China","award":["2022YFB3103600"],"award-info":[{"award-number":["2022YFB3103600"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U23A20296","62272469","72371245","62302513"],"award-info":[{"award-number":["U23A20296","62272469","72371245","62302513"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The Science and Technology Innovation Program of Hunan Province","award":["2023RC1007"],"award-info":[{"award-number":["2023RC1007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Big Data"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1109\/tbdata.2025.3566576","type":"journal-article","created":{"date-parts":[[2025,5,2]],"date-time":"2025-05-02T13:17:55Z","timestamp":1746191875000},"page":"2972-2986","source":"Crossref","is-referenced-by-count":0,"title":["Towards Privacy-Preserving Personalized Federated Relation Classification"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9870-6751","authenticated-orcid":false,"given":"Ning","family":"Pang","sequence":"first","affiliation":[{"name":"Aviation University of Air Force, Changchun, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6339-0219","authenticated-orcid":false,"given":"Xiang","family":"Zhao","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2690-3190","authenticated-orcid":false,"given":"Weixin","family":"Zeng","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8643-4683","authenticated-orcid":false,"given":"Zhen","family":"Tan","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weidong","family":"Xiao","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"2335","article-title":"Relation classification via convolutional deep neural network","volume-title":"Proc. 25th Int. Conf. Comput. Linguistics","author":"Zeng"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1206"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-emnlp.52"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.165"},{"key":"ref5","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. 20th Int. Conf. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/tit.2022.3192506"},{"key":"ref7","first-page":"10092","article-title":"Parameterized knowledge transfer for personalized federated learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref8","first-page":"1","article-title":"Personalized federated learning with first order model optimization","volume-title":"Proc. 9th Int. Conf. Learn. Representations","author":"Zhang"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3160699"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/n19-1306"},{"article-title":"Differentially private federated learning: A client level perspective","year":"2017","author":"Geyer","key":"ref11"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3056991"},{"key":"ref13","first-page":"3571","article-title":"Collecting telemetry data privately","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Ding"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591748"},{"key":"ref16","first-page":"1201","article-title":"Semantic compositionality through recursive matrix-vector spaces","volume-title":"Proc. 2012 Joint Conf. Empir. Methods Natural Lang. Process. Comput. Natural Lang. Learn.","author":"Socher"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1244"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1649"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/p19-1279"},{"key":"ref20","first-page":"2339","article-title":"Dependency-aware prototype learning for few-shot relation classification","volume-title":"Proc. Int. Conf. Comput. Linguistics","author":"Yu"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2021.3096200"},{"key":"ref22","first-page":"1","article-title":"Prototypical representation learning for relation extraction","volume-title":"Proc. 9th Int. Conf. Learn. Representations","author":"Ding"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8852464"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-29763-x"},{"article-title":"FedMD: Heterogenous federated learning via model distillation","year":"2019","author":"Li","key":"ref25"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2018.07.011"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2732482"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413507"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/308"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2020.07.009"},{"key":"ref31","first-page":"1","article-title":"Learning differentially private recurrent language models","volume-title":"Proc. Int. Conf. Learn. Representations","author":"McMahan"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2007.66"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835869"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1561\/9781601988195"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00029"},{"article-title":"Voting-based approaches for differentially private federated learning","year":"2020","author":"Zhu","key":"ref36"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.29012\/jpc.v7i3.405"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1810.04805"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.ps.32.020181.000321"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01035"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"ref42","first-page":"26048","article-title":"Understanding clipping for federated learning: Convergence and client-level differential privacy","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhang"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.3115\/1621969.1621986"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1004"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.142"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00019"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.07.098"},{"article-title":"FedNLP: A research platform for federated learning in natural language processing","year":"2021","author":"Lin","key":"ref48"}],"container-title":["IEEE Transactions on Big Data"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6687317\/11244109\/10982134.pdf?arnumber=10982134","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T18:45:34Z","timestamp":1763059534000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10982134\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":48,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/tbdata.2025.3566576","relation":{},"ISSN":["2332-7790","2372-2096"],"issn-type":[{"type":"electronic","value":"2332-7790"},{"type":"electronic","value":"2372-2096"}],"subject":[],"published":{"date-parts":[[2025,12]]}}}