Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatic
Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatics閱讀筆記 文獻背景及解決問題 車聯網中的聯邦學習 具體方案 總結與思考 文獻背景及解決問題 由於無線網絡帶寬和計算資源的限制，車輛很難使用大量數據來進行提高服務質量的機器學習，比如自動駕駛和交通預測
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- 1. Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatic
- 2. 論文筆記——Federated learning framework for mobile edge computing networks
- 3. Federated Learning in Mobile Edge Networks: AComprehensive Survey(翻譯)
- 4. Differentially Private Federated Learning: A Client Level Perspective
- 5. 論文筆記——Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
- 6. Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
- 7. Deployment Characteristics of "The Edge" in Mobile Edge Computing
- 8. 【論文閱讀】Edge Computing Architecture for Mobile Crowdsensing
- 9. Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach
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相關文章
- 1. Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatic
- 2. 論文筆記——Federated learning framework for mobile edge computing networks
- 3. Federated Learning in Mobile Edge Networks: AComprehensive Survey(翻譯)
- 4. Differentially Private Federated Learning: A Client Level Perspective
- 5. 論文筆記——Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
- 6. Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
- 7. Deployment Characteristics of "The Edge" in Mobile Edge Computing
- 8. 【論文閱讀】Edge Computing Architecture for Mobile Crowdsensing
- 9. Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach
- 10. Edge Computing