{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T07:33:27Z","timestamp":1763192007954,"version":"3.45.0"},"reference-count":30,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T00:00:00Z","timestamp":1751241600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T00:00:00Z","timestamp":1751241600000},"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","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006190","name":"Research and Development","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006190","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,6,30]]},"DOI":"10.1109\/ijcnn64981.2025.11229156","type":"proceedings-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T18:46:15Z","timestamp":1763145975000},"page":"1-9","source":"Crossref","is-referenced-by-count":0,"title":["FedHG: Generator-Driven Head Adaptive Aggregation for Personalized Federated Learning"],"prefix":"10.1109","author":[{"given":"MingKai","family":"Hu","sequence":"first","affiliation":[{"name":"Henan Univ. of Sci. & Tech,School of Mathematics and Statistics,Luoyang,China"}]},{"given":"Ping","family":"Zhang","sequence":"additional","affiliation":[{"name":"Henan Univ. of Sci. & Tech,School of Mathematics and Statistics,Luoyang,China"}]},{"given":"Hesheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Henan Univ. of Sci. & Tech,School of Mathematics and Statistics,Luoyang,China"}]},{"given":"Yijing","family":"Xie","sequence":"additional","affiliation":[{"name":"Henan Univ. of Sci. & Tech,School of Mathematics and Statistics,Luoyang,China"}]},{"given":"Nan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Henan Univ. of Sci. & Tech,School of Mathematics and Statistics,Luoyang,China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3659205"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.103061"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/NOMS59830.2024.10575333"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00323-1"},{"key":"ref5","first-page":"1273","article-title":"Communication-Efficient Learning of Deep Networks from Decentralized Data","volume-title":"Proc. 20th Int. Conf. Artif. Intell. Stat(PMLR)","author":"McMahan"},{"article-title":"Federated Optimization in Heterogeneous Networks","year":"2020","author":"Li","key":"ref6"},{"key":"ref7","first-page":"12878","article-title":"Data-Free Knowledge Distillation for Heterogeneous Federated Learning","volume-title":"Proc. 38th Int. Conf. Mach. Learn","volume":"139","author":"Zhu"},{"article-title":"Improving federated learning personalization via model agnostic meta learning","year":"2019","author":"Jiang","key":"ref8"},{"article-title":"Personalized Federated Learning with First Order Model Optimization","year":"2021","author":"Zhang","key":"ref9"},{"key":"ref10","first-page":"2089","article-title":"Exploiting Shared Representations for Personalized Federated Learning","volume-title":"Proc. 38th Int. Conf. Mach. Learn. (PMLR)","author":"Collins"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s00530-025-01719-3"},{"key":"ref12","first-page":"3557","article-title":"Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach","volume":"33","author":"Fallah","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-021-09587-6"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2023.3261822"},{"key":"ref16","article-title":"Generating videos with scene dynamics","volume-title":"Advances in Neural Information Processing Systems","volume":"29","author":"Vondrick","year":"2016"},{"article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","year":"2018","author":"Devlin","key":"ref17"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3294090"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.3390\/math12101601"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2025\/692"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/301"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26330"},{"article-title":"Federated Learning with Personalization Layers","year":"2019","author":"Arivazhagan","key":"ref23"},{"article-title":"FedBABU: Towards Enhanced Representation for Federated Image Classification","year":"2022","author":"Oh","key":"ref24"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00993"},{"key":"ref26","article-title":"How transferable are features in deep neural networks?","volume-title":"Advances in Neural Information Processing Systems","volume":"27","author":"Yosinski","year":"2014"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"article-title":"Learning multiple layers of features from tiny images","year":"2009","author":"Krizhevsky","key":"ref28"},{"article-title":"A downsampled variant of ImageNet as an alternative to the CIFAR datasets","year":"2017","author":"Chrabaszcz","key":"ref29"},{"key":"ref30","first-page":"2351","article-title":"Ensemble distillation for robust model fusion in federated learning","volume":"33","author":"Lin","year":"2020","journal-title":"Advances in Neural Information Processing Systems"}],"event":{"name":"2025 International Joint Conference on Neural Networks (IJCNN)","start":{"date-parts":[[2025,6,30]]},"location":"Rome, Italy","end":{"date-parts":[[2025,7,5]]}},"container-title":["2025 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11227166\/11227148\/11229156.pdf?arnumber=11229156","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T07:30:13Z","timestamp":1763191813000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11229156\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,30]]},"references-count":30,"URL":"https:\/\/doi.org\/10.1109\/ijcnn64981.2025.11229156","relation":{},"subject":[],"published":{"date-parts":[[2025,6,30]]}}}