Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach
論文解讀：html 背景 開銷和隱私兩大problems 移動網絡在計算和通訊兩方面有極大的開銷，若是沒有激勵機制，那麼感興趣的移動設備就不肯意加入聯邦學習任務web 以往的假設是認爲全部的移動設備都會在被邀請時無條件的參與聯邦學習，可是沒有精心設計的補償，自私自利的移動設備將不肯意參與網絡 兩個信息不對成 1.任務發佈者不知道用於模型訓練的資源量和數據大小 2.不知道移動設備的數據質量 致使：
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相關文章
- 1. Federated Learning in Mobile Edge Networks: AComprehensive Survey(翻譯)
- 2. An Efficient Deep Learning Approach for Collaborative Filtering Recommender System
- 3. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
- 4. Geoffrey Hinton's Capsule Networks: A Novel Approach to Deep Learning
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- 6. Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
- 7. Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatic
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- 10. 論文泛讀 A Novel Ensemble Learning-based Approach for Click Fraud Detection in Mobile Advertising [1/10]