Split Federated Learning for Secure IoT Applications : Concepts, Frameworks, Applications and Case Studies.

Format:

Book

Author/Creator:

Lokesh, Gururaj Harinahalli.

Contributor:

Hukkeri, Geetabai S.

Jhanjhi, N. Z.

Lin, Hong.

Series:

Security Series

Language:

English

Subjects (All):

Internet of things.

Data protection.

Physical Description:

1 online resource (269 pages)

Edition:

1st ed.

Place of Publication:

Stevenage : Institution of Engineering & Technology, 2024.

Summary:

This book will review cutting edge technologies and advanced research, which can realize and evaluate the effectiveness and advantages of SplitFed learning for advancing and securing IoTs.

Contents:

Cover

Title

Copyright

Contents

Preface

About the editors

1. Introduction to federated learning, split learning and splitfed learning

1.1 Introduction

1.2 Related works

1.2.1 Federated learning

1.2.2 Split learning

1.2.3 Split-federated learning

1.3 Splitfed learning: combining federated learning and split learning

1.3.1 Why splitfed learning

1.3.2 Applications of splitfed learning

1.4 Conclusion

References

2. Splitfed learning processing for IoT and Big Data applications

2.1 Introduction

2.2 Fundamentals of splitfed learning

2.2.1 Distributed learning

2.2.2 Federated learning

2.2.3 Split learning

2.3 Challenges in IoT and Big Data environments

2.4 Applications of splitfed learning in IoT

2.5 Applications of splitfed learning in Big Data

2.6 Methodology of split federated learning

2.7 Performance evaluation and benchmarking in splitfed learning

2.8 Conclusion

References Generated by AI.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

1-83724-382-4

1-83953-946-1

OCLC:

1458760853