| Description |
1 online resource |
| Contents |
Front Cover -- Federated Learning -- Copyright -- Contents -- Contributors -- Preface -- 1 Optimization fundamentals for secure federated learning -- 1 Gradient descent-type methods -- 1.1 Introduction -- 1.2 Basic components of GD-type methods -- 1.2.1 Search direction -- 1.2.2 Step-size -- 1.2.3 Proximal operator -- 1.2.4 Momentum -- 1.2.5 Dual averaging variant -- 1.2.6 Structure assumptions -- 1.2.7 Optimality certification -- 1.2.8 Unified convergence analysis -- 1.2.9 Convergence rates and complexity analysis -- 1.2.10 Initial point, warm-start, and restart |
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1.3 Stochastic gradient descent methods -- 1.3.1 The algorithmic template -- 1.3.2 SGD estimators -- 1.3.3 Unified convergence analysis -- 1.4 Concluding remarks -- Acknowledgments -- References -- 2 Considerations on the theory of training models with differential privacy -- 2.1 Introduction -- 2.2 Differential private SGD (DP-SGD) -- 2.2.1 Clipping -- 2.2.2 Mini-batch SGD -- 2.2.3 Gaussian noise -- 2.2.4 Aggregation at the server -- 2.2.5 Interrupt service routine -- 2.2.6 DP principles and utility -- 2.2.7 Normalization -- 2.3 Differential privacy |
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3 Privacy-preserving federated learning: algorithms and guarantees -- 3.1 Introduction -- 3.2 Background and preliminaries -- 3.2.1 The FedAvg algorithm -- 3.2.2 Differential privacy -- 3.3 DP guaranteed algorithms -- 3.3.1 Sample-level DP -- 3.3.1.1 Algorithms and discussion -- 3.3.2 Client-level DP -- 3.3.2.1 Clipping strategies for client-level DP -- 3.3.2.2 Algorithms and discussion -- 3.4 Performance of clip-enabled DP-FedAvg -- 3.4.1 Main results -- 3.4.1.1 Convergence theorem -- 3.4.1.2 DP guarantee -- 3.4.2 Experimental evaluation -- 3.5 Conclusion and future work -- References |
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4 Assessing vulnerabilities and securing federated learning -- 4.1 Introduction -- 4.2 Background and vulnerability analysis -- 4.2.1 Definitions and notation -- 4.2.1.1 Horizontal federated learning -- 4.2.1.2 Vertical federated learning -- 4.2.2 Vulnerability analysis -- 4.2.2.1 Clients' updates -- 4.2.2.2 Repeated interaction -- 4.3 Attacks on federated learning -- 4.3.1 Training-time attacks -- 4.3.1.1 Byzantine attacks -- 4.3.1.2 Backdoor attacks -- 4.3.2 Inference-time attacks -- 4.4 Defenses -- 4.4.1 Protecting against training-time attacks -- 4.4.1.1 In Situ defenses |
| Subject |
Machine learning.
|
|
Apprentissage automatique.
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| Added Author |
Nguyen, Lam M.
|
|
Hoang, Trong Nghia.
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Chen, Pin-Yu.
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| Other Form: |
Print version: 0443190372 9780443190377 (OCoLC)1385448257 |
| ISBN |
9780443190384 (electronic bk.) |
|
0443190380 (electronic bk.) |
|
9780443190377 |
|
0443190372 |
| Standard No. |
AU@ 000076170802 |
|
