Differentially Private Federated Learning: A Client Level Perspective
介紹 咱們的目標並非只保護數據。相反，咱們但願確保一個學習模型不會顯示客戶是否參與了分散的培訓。這意味着客戶機的整個數據集受到保護，不受來自其餘客戶機的差別攻擊。 咱們的主要貢獻:首先，咱們展現了在聯合學習中保持較高的模型性能時，客戶機的參與是能夠隱藏的。咱們證實了咱們提出的算法能夠在模型性能損失很小的狀況下實現客戶級差別隱私。與此同時發表的一項獨立研究[6]提出了一個相似的客戶級dp程序。然而，
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相關文章
- 1. Differentially Private Federated Learning: A Client Level Perspective
- 2. Differentially Private Asynchronous Federated Learning for Mobile Edge Computing in Urban Informatic
- 3. Machine Learning A Probabilistic Perspective 1.Introduction
- 4. A Distributional Perspective on Reinforcement Learning
- 5. Note: Differentially Private Access Patterns for Searchable Symmetric Encryption
- 6. 論文筆記Client-Edge-Cloud Hierarchical Federated Learning
- 7. 【資源】Machine Learning A Bayesian_and Optimization Perspective(MLBOP)
- 8. SecureBoost: A Lossless Federated Learning Framework論文筆記
- 9. Federated Learning: 相關
- 10. [轉載] Conv Nets: A Modular Perspective