Federated learning theory and practice / edited by Lam M. Nguyen, Trong Nghia Hoang and Pin-Yu Chen.

Format:

Book

Contributor:

Nguyen, Lam M.

Hoang, Trong Nghia.

Chen, Pin-Yu.

ProQuest ebook central

Language:

English

Subjects (All):

Machine learning.

Physical Description:

1 online resource

Place of Publication:

London : Academic Press, 2024.

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

Notes:

Electronic reproduction. Ann Arbor, MI Available via World Wide Web.

Other Format:

Print version:

ISBN:

9780443190384

0443190380

Publisher Number:

40032201204

Access Restriction:

Restricted for use by site license.