{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T07:23:25Z","timestamp":1763191405306,"version":"3.45.0"},"reference-count":33,"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\/100006190","name":"Research and Development","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006190","id-type":"DOI","asserted-by":"publisher"}]},{"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\/ijcnn64981.2025.11227627","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":["FedPTR: Enhancing Federated Prompt Learning with Server-Side Retraining for Non-IID Data"],"prefix":"10.1109","author":[{"given":"Yufeng","family":"Chen","sequence":"first","affiliation":[{"name":"University of Chinese Academy of Sciences,School of Computer Science and Technology,Beijing,China"}]},{"given":"Min","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences,School of Computer Science and Technology,Beijing,China"}]},{"given":"Sheng","family":"Sun","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences,Institute of Computing Technology,Beijing,China"}]},{"given":"Zhongcheng","family":"Li","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences,School of Computer Science and Technology,Beijing,China"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Artificial intelligence and statistics","author":"McMahan","year":"2017"},{"article-title":"A survey on efficient federated learning methods for foundation model training","year":"2024","author":"Woisetschlager","key":"ref2"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2023.3302410"},{"key":"ref4","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"International Conference on Machine Learning","author":"Radford"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-022-01653-1"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/3560815"},{"article-title":"Measuring the effects of non-identical data distribution for federated visual classification","year":"2019","author":"Hsu","key":"ref7"},{"key":"ref8","first-page":"4519","article-title":"Tighter theory for local sgd on identical and heterogeneous data","volume-title":"International Conference on Artificial Intelligence and Statistics","author":"Khaled"},{"author":"Lee","key":"ref9","article-title":"Preservation of the global knowledge by not-true self knowledge distillation in federated learning. arxiv 2021"},{"key":"ref10","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proceedings of Machine learning and systems","volume":"2","author":"Li"},{"key":"ref11","first-page":"5132","article-title":"Scaffold: Stochastic controlled averaging for federated learning","volume-title":"International Conference on Machine Learning","author":"Karimireddy"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"ref13","first-page":"12878","article-title":"Data-free knowledge distillation for heterogeneous federated learning","volume-title":"International Conference on Machine Learning","author":"Zhu"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/tc.2023.3315066"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3319986"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00993"},{"key":"ref17","article-title":"Dfrd: Data-free robustness distillation for heterogeneous federated learning","volume":"36","author":"Wang","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583518"},{"article-title":"Mixture of experts made personalized: Federated prompt learning for vision-language models","year":"2024","author":"Luo","key":"ref19"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01755"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01155"},{"article-title":"Harmonizing generalization and personalization in federated prompt learning","year":"2024","author":"Cui","key":"ref22"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01631"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2005.09.012"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.461"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10599-4_29"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248092"},{"article-title":"Learning multiple layers of features from tiny images","year":"2009","author":"Krizhevsky","key":"ref29"},{"key":"ref30","first-page":"9489","article-title":"Personalized federated learning using hypernetworks","volume-title":"International Conference on Machine Learning","author":"Shamsian"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2023.3269062"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.02036"},{"article-title":"Flgo: A fully customizable federated learning platform","year":"2023","author":"Wang","key":"ref33"}],"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\/11227627.pdf?arnumber=11227627","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T07:20:00Z","timestamp":1763191200000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11227627\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,30]]},"references-count":33,"URL":"https:\/\/doi.org\/10.1109\/ijcnn64981.2025.11227627","relation":{},"subject":[],"published":{"date-parts":[[2025,6,30]]}}}