Enhancing Federated Learning with Intelligent Model Migration in Heterogeneous Edge Computing
Author:
Affiliation:
1. School of Data Science, University of Science and Technology of China,China
2. School of Computer Science and Technology, University of Science and Technology of China,China
3. School of Computer Science and Technology, Soochow University,China
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9835153/9835154/09835657.pdf?arnumber=9835657
Cited by 13 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. Ferrari: A Personalized Federated Learning Framework for Heterogeneous Edge Clients;IEEE Transactions on Mobile Computing;2024-10
2. Federated Learning With Experience-Driven Model Migration in Heterogeneous Edge Networks;IEEE/ACM Transactions on Networking;2024-08
3. Heroes: Lightweight Federated Learning with Neural Composition and Adaptive Local Update in Heterogeneous Edge Networks;IEEE INFOCOM 2024 - IEEE Conference on Computer Communications;2024-05-20
4. FedCross: Towards Accurate Federated Learning via Multi-Model Cross-Aggregation;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13
5. MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13
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