論文筆記——Federated learning framework for mobile edge computing networks
論文筆記——Federated learning framework for mobile edge computing networks 本論文着重研究的是聯邦學習應用於需求預測類問題。 一般來說，FL存在的一些問題： 非獨立同分布數據。客戶訓練數據集各不相同，給定的本地訓練數據集不代表人口分佈。 不平衡數據集。每個客戶的本地訓練數據量不同。這意味着不同客戶對訓練價值觀的可靠性不同，因爲訓練程序
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- 1. 論文筆記——Federated learning framework for mobile edge computing networks
- 2. 論文筆記——Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
- 3. Federated Learning in Mobile Edge Networks: AComprehensive Survey(翻譯)
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- 5. 論文筆記Client-Edge-Cloud Hierarchical Federated Learning
- 6. 【論文閱讀】Edge Computing Architecture for Mobile Crowdsensing
- 7. SecureBoost: A Lossless Federated Learning Framework論文筆記
- 8. Secure Federated Transfer Learning（論文筆記）
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- 10. 論文筆記——Model Pruning Enables Efficient Federated Learning on Edge Devices
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相關文章
- 1. 論文筆記——Federated learning framework for mobile edge computing networks
- 2. 論文筆記——Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
- 3. Federated Learning in Mobile Edge Networks: AComprehensive Survey(翻譯)
- 4. Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatic
- 5. 論文筆記Client-Edge-Cloud Hierarchical Federated Learning
- 6. 【論文閱讀】Edge Computing Architecture for Mobile Crowdsensing
- 7. SecureBoost: A Lossless Federated Learning Framework論文筆記
- 8. Secure Federated Transfer Learning（論文筆記）
- 9. DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics
- 10. 論文筆記——Model Pruning Enables Efficient Federated Learning on Edge Devices