論文筆記——Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
論文筆記——Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge 本文提出了一種基於邊緣計算的協議來改進聯邦學習算法。算法 由服務器和基站（BS）組成的位於無線網絡中的特定MEC平臺管理服務器和客戶端的行爲。服務器 經過MEC operator 對客戶端進行選擇。網絡 首先，隨機選取必
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- 1. 論文筆記——Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
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相關文章
- 1. 論文筆記——Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
- 2. Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
- 3. 論文筆記Client-Edge-Cloud Hierarchical Federated Learning
- 4. 論文筆記——Federated learning framework for mobile edge computing networks
- 5. Federated Learning in Mobile Edge Networks: AComprehensive Survey(翻譯)
- 6. Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatic
- 7. Federated Learning with Non-IID Data 論文筆記
- 8. 論文筆記——Fair Resource Allocation in Federated Learning
- 9. Secure Federated Transfer Learning（論文筆記）
- 10. 論文：Learning Matching Models with Weak Supervision for Response Selection in Retrieval-based Chatbots