Federated Learning: Fundamentals and Advances - Couverture souple

Jin, Yaochu; Zhu, Hangyu; Xu, Jinjin

Couverture souple

ISBN 10 : 981197084X ISBN 13 : 9789811970849

Editeur : Springer, 2022

Synopsis

1. Introduction

1.1 Artificial neural networks and deep learning

1.2 Evolutionary optimization and learning

1.3 Privacy-preserving computation

1.4 Federated learning

1.5 Summary

1. Communication-Efficient Federated Learning

2.1 Communication cost in federated learning

2.2 Main methodologies

2.3 Temporally weighted averaging and layer-wise weight update

2.4 Trained ternary compression for federated learning

2.5 Summary

Evolutionary Federated Learning

3.1 Motivations and challenges

3.2 Offline evolutionary multi-objective federated learning

3.3 Realtime evolutionary federated neural architecture search

3.4 Summary

Secure Federated Learning

4.1 Threats to federated learning

4.2 Distributed encryption for horizontal federated learning

4.3 Secure vertical federated learning

4.4 Summary

Summary and Outlook

5.1 Summary

5.2 Future directions

Détails de l'édition

• Éditeur: Springer
• Date d'édition: 2022
• Langue: anglais
• ISBN 10: 981197084X
• ISBN 13: 9789811970849
• Reliure: Broché
• Nombre de pages: 232
• Coordonnées du fabricant: non disponible
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