{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T07:43:18Z","timestamp":1763192598036,"version":"3.45.0"},"reference-count":40,"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"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,6,30]]},"DOI":"10.1109\/ijcnn64981.2025.11228147","type":"proceedings-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T18:46:15Z","timestamp":1763145975000},"page":"1-8","source":"Crossref","is-referenced-by-count":0,"title":["Personalized Federated Learning Based on Feature Filtering"],"prefix":"10.1109","author":[{"given":"Zhangshuai","family":"Bie","sequence":"first","affiliation":[{"name":"Xinjiang University,Urumqi,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linlin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Xinjiang University,Urumqi,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Congjie","family":"Lin","sequence":"additional","affiliation":[{"name":"Xinjiang University,Urumqi,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuo","family":"Li","sequence":"additional","affiliation":[{"name":"Xinjiang University,Urumqi,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenshou","family":"Wu","sequence":"additional","affiliation":[{"name":"Xinjiang University,Urumqi,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Zhao","sequence":"additional","affiliation":[{"name":"Xinjiang University,Urumqi,China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proceedings of the 20th Artificial Intelligence and Statistics","author":"McMahan"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3269062"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944481"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2021.3104339"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2020.3021713"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"article-title":"Mamba: Linear-time sequence modeling with selective state spaces","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Gu","key":"ref7"},{"article-title":"VMamba: Visual state space model","volume-title":"Proceedings of the 38th International Conference on Neural Information Processing Systems","author":"Liu","key":"ref8"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s44267-024-00072-9"},{"article-title":"Demystify mamba in vision: A linear attention perspective","volume-title":"Proceedings of the 38th International Conference on Neural Information Processing Systems","author":"Han","key":"ref10"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539254"},{"key":"ref12","first-page":"3557","article-title":"Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach","volume-title":"Proceedings of the 34th Conference on Neural Information Processing Systems","volume":"33","author":"Fallah"},{"key":"ref13","first-page":"21394","article-title":"Personalized federated learning with moreau envelopes","volume-title":"Proceedings of the 34th Conference on Neural Information Processing Systems","volume":"33","author":"Dinh"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i9.16960"},{"key":"ref15","first-page":"6357","article-title":"Ditto: Fair and robust federated learning through personalization","volume-title":"Proceedings of the 38th International Conference on Machine Learning","volume":"139","author":"Li"},{"article-title":"Federated learning with personalization layers","year":"2019","author":"Arivazhagan","key":"ref16"},{"key":"ref17","first-page":"2089","article-title":"Exploiting shared representations for personalized federated learning","volume-title":"Proceedings of the 38th International Conference on Machine Learning","volume":"139","author":"Collins"},{"article-title":"On bridging generic and personalized federated learning for image classification","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Chen","key":"ref18"},{"key":"ref19","first-page":"4175","article-title":"Balanced meta-softmax for long-tailed visual recognition","volume-title":"Proceedings of the 34th International Conference on Neural Information Processing Systems","author":"Ren"},{"article-title":"Personalized federated learning with first order model optimization","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Zhang","key":"ref20"},{"article-title":"Personalized federated learning with feature alignment and classifier collaboration","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Xu","key":"ref21"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86486-6_36"},{"article-title":"Pointdan: A multi-scale 3d domain adaption network for point cloud representation","volume-title":"Proceedings of the International Conference on Advances in Neural Information Processing Systems","author":"Qin","key":"ref23"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26330"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599345"},{"key":"ref26","first-page":"11863","article-title":"SimAM: A simple, parameter-free attention module for convolutional neural networks","volume-title":"Proceedings of the 38th International Conference on Machine Learning","volume":"139","author":"Yang"},{"article-title":"Gaussian error linear units (GELUs)","year":"2016","author":"Hendrycks","key":"ref27"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-04898-2_327"},{"article-title":"No fear of heterogeneity: Classifier calibration for federated learning with non-iid data","volume-title":"Proceedings of the International Conference on Advances in Neural Information Processing Systems","author":"Luo","key":"ref29"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01167"},{"article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Dosovitskiy","key":"ref31"},{"article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","year":"2017","author":"Xiao","key":"ref32"},{"key":"ref33","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009","journal-title":"Technical Report"},{"article-title":"Personalized federated learning using hypernetworks","volume-title":"Proceedings of 38th the International Conference on Machine Learning","author":"Shamsian","key":"ref34"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"issue":"198","key":"ref36","first-page":"2351","article-title":"Ensemble distillation for robust model fusion in federated learning","volume-title":"Proceedings of the 34th International Conference on Neural Information Processing Systems","author":"Lin"},{"key":"ref37","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proceedings of the Conference on Machine Learning and Systems","volume":"2","author":"Li"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICAICE51518.2020.00038"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20819"},{"article-title":"FedBN: Federated learning on non-iid features via local batch normalization","year":"2021","author":"Li","key":"ref40"}],"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\/11228147.pdf?arnumber=11228147","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T07:39:42Z","timestamp":1763192382000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11228147\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,30]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/ijcnn64981.2025.11228147","relation":{},"subject":[],"published":{"date-parts":[[2025,6,30]]}}}