{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T01:53:30Z","timestamp":1772934810386,"version":"3.50.1"},"reference-count":22,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,8]]},"DOI":"10.1109\/bigdata66926.2025.11400968","type":"proceedings-article","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T20:57:57Z","timestamp":1772830677000},"page":"4263-4268","source":"Crossref","is-referenced-by-count":0,"title":["Personalized Federated Learning with Shared Feature Tables"],"prefix":"10.1109","author":[{"given":"Yong","family":"Li","sequence":"first","affiliation":[{"name":"Changchun University of Technology,Changchun,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"YiMing","family":"Wang","sequence":"additional","affiliation":[{"name":"Jilin Province Computing Center,Changchun,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han","family":"Gao","sequence":"additional","affiliation":[{"name":"Changchun University of Technology,Changchun,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuo","family":"Yang","sequence":"additional","affiliation":[{"name":"Jilin Province Computing Center,Changchun,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"YongXin","family":"Jiang","sequence":"additional","affiliation":[{"name":"Non-commissioned Officer School of Army Academy of Armored Forces,Changchun,China,130051"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data[C]","volume-title":"Artificial intelligence and statistics","author":"McMahan","year":"2017"},{"key":"ref2","first-page":"3557","article-title":"Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach[J]","volume":"33","author":"Fallah","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref3","first-page":"429","article-title":"Federated optimization in heterogeneous networks[J]","volume-title":"Proceedings of Machine learning and systems","volume":"2","author":"Li","year":"2020"},{"key":"ref4","author":"Arivazhagan","year":"2019","journal-title":"Federated learning with personalization layers[J]"},{"key":"ref5","first-page":"400","author":"Dinh","year":"2021","journal-title":"Fedu: A unified framework for federated multi-task learning with laplacian regularization[J]"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref7","author":"Reddi","year":"2020","journal-title":"Adaptive federated optimization[J]"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICPADS56603.2022.00107"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/APPEEC53445.2022.10072290"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9005465"},{"key":"ref11","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data[C]","volume-title":"Artificial intelligence and statistics","author":"McMahan","year":"2017"},{"key":"ref12","author":"Tan","year":"2023","journal-title":"pfedsim: Similarity-aware model aggregation towards personalized federated learning[J]"},{"key":"ref13","author":"Deng","year":"2020","journal-title":"Adaptive personalized federated learning[J]"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26330"},{"key":"ref15","author":"Niu","year":"2019","journal-title":"Secure federated submodel learning[J]"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102993"},{"issue":"11","key":"ref17","first-page":"2278","article-title":"Gradient-based learning applied to document recognition[J]","volume-title":"Proceedings of the IEEE","volume":"86","author":"Le Cun","year":"1998"},{"key":"ref18","author":"Yi","year":"2023","journal-title":"pfedes: Model heterogeneous personalized federated learning with feature extractor sharing[J]"},{"key":"ref19","first-page":"2351","article-title":"Ensemble distillation for robust model fusion in federated learning[J]","volume":"33","author":"Lin","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2026.3656194"},{"key":"ref21","author":"Krizhevsky","year":"2009","journal-title":"Learning multiple layers of features from tiny images[J]"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3545008.3545073"}],"event":{"name":"2025 IEEE International Conference on Big Data (BigData)","location":"Macau, China","start":{"date-parts":[[2025,12,8]]},"end":{"date-parts":[[2025,12,11]]}},"container-title":["2025 IEEE International Conference on Big Data (BigData)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11400704\/11400712\/11400968.pdf?arnumber=11400968","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T07:17:39Z","timestamp":1772867859000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11400968\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,8]]},"references-count":22,"URL":"https:\/\/doi.org\/10.1109\/bigdata66926.2025.11400968","relation":{},"subject":[],"published":{"date-parts":[[2025,12,8]]}}}