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Federated learning : theory and practice /

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 vario...

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Bibliographic Details
Corporate Author: ScienceDirect (Online service)
Other Authors: Nguyen, Lam M., Hoang, Trong Nghia (Computer scientist), Chen, Pin-Yu
Format: eBook
Language:English
Published: London : Academic Press, 2024.
Subjects:
Machine learning.
Apprentissage automatique.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of 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
  • 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
  • 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
  • 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