| 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 |
| Summary |
Federated Learning: Theory and Practi ce provides a holisti c treatment to federated learning as a distributed learning system with various forms of decentralized data and features. Part I of the book begins with a broad overview of opti mizati on fundamentals and modeling challenges, covering various aspects of communicati on effi ciency, theoretical convergence, and security. Part II features emerging challenges stemming from many socially driven concerns of federated learning as a future public machine learning service. Part III concludes the book with a wide array of industrial applicati ons of federated learning, as well as ethical considerations, showcasing its immense potential for driving innovation while safeguarding sensitive data. Federated Learning: Theory and Practi ce provides a comprehensive and accessible introducti on to federated learning which is suitable for researchers and students in academia, and industrial practitioners who seek to leverage the latest advance in machine learning for their entrepreneurial endeavors. Presents the fundamentals and a survey of key developments in the field of federated learning Provides emerging, state-of-the art topics that build on fundamentals Contains industry applications Gives an overview of visions of the future. |
| Subject |
Machine learning.
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Apprentissage automatique.
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| Added Author |
Nguyen, Lam M.
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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 |
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0443190372 |
| Standard No. |
AU@ 000076170802 |
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