GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning

Author:

Zhang Jianqing1,Hua Yang2,Wang Hao3,Song Tao1,Xue Zhengui1,Ma Ruhui1,Cao Jian1,Guan Haibing1

Affiliation:

1. Shanghai Jiao Tong University

2. Queen’s University Belfast

3. Louisiana State University

Funder

China Aerospace Science and Technology Corporation

Science and Technology Commission of Shanghai Municipality

Publisher

IEEE

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. ChronusFed: Reinforcement-Based Adaptive Partial Training for Heterogeneous Federated Learning;Proceedings of the 53rd International Conference on Parallel Processing;2024-08-12

2. Personalized Federated Learning with Enhanced Implicit Generalization;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

3. Enhancing Both Generalization and Personalization in Personalized Federated Learning via Global and Local Knowledge Distillation;2024 5th International Conference on Information Science, Parallel and Distributed Systems (ISPDS);2024-05-31

4. Improving Generalization and Personalization in Model-Heterogeneous Federated Learning;IEEE Transactions on Neural Networks and Learning Systems;2024

5. Controlled Privacy Leakage Propagation Throughout Overlapping Grouped Learning;IEEE Journal on Selected Areas in Information Theory;2024

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