Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge 摘要 我們設想了一個用於機器學習（ML）技術的移動邊緣計算（MEC）框架，它利用分佈式客戶端數據和計算資源來訓練高性能ML模型，同時保留客戶端隱私。爲了實現這一未來目標，本文旨在擴展聯邦學習（FL）這個分散學習框架，使模型的隱私保護
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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. Federated Learning in Mobile Edge Networks: AComprehensive Survey(翻譯)
- 3. Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatic
- 4. 論文筆記Client-Edge-Cloud Hierarchical Federated Learning
- 5. 論文筆記——Federated learning framework for mobile edge computing networks
- 6. Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach
- 7. 【譯】Federated Learning: Bringing Machine Learning to the edge with Kotlin and Android
- 8. 31 Game-Based Learning Resources for Educators
- 9. DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics
- 10. Energy-efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks----邊緣計算譯文part I