{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:02:03Z","timestamp":1777888923443,"version":"3.51.4"},"reference-count":34,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"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":[[2025,10,19]]},"DOI":"10.1109\/iccv51701.2025.00298","type":"proceedings-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T19:45:49Z","timestamp":1777491949000},"page":"3111-3120","source":"Crossref","is-referenced-by-count":0,"title":["FedPall: Prototype-Based Adversarial and Collaborative Learning for Federated Learning with Feature Drift"],"prefix":"10.1109","author":[{"given":"Yong","family":"Zhang","sequence":"first","affiliation":[{"name":"Artificial Intelligence Research Institute, Shenzhen MSU-BIT University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Liang","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Institute, Shenzhen MSU-BIT University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guanghu","family":"Yuan","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Institute, Shenzhen MSU-BIT University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Yang","sequence":"additional","affiliation":[{"name":"Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengming","family":"Li","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Institute, Shenzhen MSU-BIT University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiping","family":"Hu","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Institute, Shenzhen MSU-BIT University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00447"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01148"},{"key":"ref3","first-page":"2089","article-title":"Exploiting shared representations for personalized federated learning","volume-title":"International conference on machine learning","author":"Collins","year":"2021"},{"key":"ref4","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":"ref5","first-page":"1180","article-title":"Unsupervised domain adaptation by backpropagation","volume-title":"International conference on machine learning","author":"Ganin","year":"2015"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6247911"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2024.3402361"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/34.291440"},{"key":"ref10","article-title":"Improving federated learning personalization via model agnostic meta learning","author":"Jiang","year":"2019","journal-title":"arxiv preprint arxiv"},{"issue":"6","key":"ref11","article-title":"Scaffold: Stochastic controlled averaging for ondevice federated learning","volume":"2","author":"Karimireddy","year":"2019","journal-title":"arxiv preprint arxiv"},{"key":"ref12","article-title":"Federated optimization: Distributed optimization beyond the datacenter","author":"Kone\u010dn\u1ef3","year":"2015","journal-title":"arxiv preprint arxiv"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.3390\/s22218475"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.591"},{"key":"ref16","first-page":"10713","article-title":"Modelcontrastive federated learning","volume-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","author":"Li","year":"2021"},{"key":"ref17","first-page":"19504","article-title":"Adversarial collaborative learning on non-iid features","volume-title":"International Conference on Machine Learning","author":"Li","year":"2023"},{"key":"ref18","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proceedings of Machine learning and systems","volume":"2","author":"Li","year":"2020"},{"key":"ref19","article-title":"Fedbn: Federated learning on non-iid features via local batch normalization","volume-title":"International Conference on Learning Representations.","author":"Li"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2024.3376697"},{"key":"ref21","article-title":"Dataefficient mutual information neural estimator","author":"Lin","year":"2019","journal-title":"arxiv preprint arxiv"},{"key":"ref22","first-page":"1273","article-title":"Communicationefficient learning of deep networks from decentralized data","volume-title":"Artificial intelligence and statistics","author":"McMahan","year":"2017"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2023.01.019"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.2118\/18761-MS"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/jiot.2023.3320250"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3340109"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20819"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.52202\/068431-1405"},{"key":"ref29","article-title":"Taming cross-domain representation variance in federated prototype learning with heterogeneous data domains","author":"Wang","year":"2024","journal-title":"arxiv preprint arxiv"},{"key":"ref30","article-title":"Federated prototype-based contrastive learning for privacy-preserving cross-domain recommendation","author":"Wang","year":"2024","journal-title":"arxiv preprint arxiv"},{"key":"ref31","article-title":"Federated learning with non-iid data","author":"Zhao","year":"2018","journal-title":"arxiv preprint arxiv"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58517-4_33"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2023.3325366"},{"key":"ref34","first-page":"12878","article-title":"Data-free knowledge distillation for heterogeneous federated learning","volume-title":"International conference on machine learning","author":"Zhu","year":"2021"}],"event":{"name":"2025 IEEE\/CVF International Conference on Computer Vision (ICCV)","location":"Honolulu, HI, USA","start":{"date-parts":[[2025,10,19]]},"end":{"date-parts":[[2025,10,25]]}},"container-title":["2025 IEEE\/CVF International Conference on Computer Vision (ICCV)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11443115\/11443287\/11445199.pdf?arnumber=11445199","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T05:02:36Z","timestamp":1777611756000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11445199\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,19]]},"references-count":34,"URL":"https:\/\/doi.org\/10.1109\/iccv51701.2025.00298","relation":{},"subject":[],"published":{"date-parts":[[2025,10,19]]}}}