Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms

Author ORCID Identifier

https://orcid.org/0000-0002-9956-4003 : Ehsan Hallaji

Document Type

Contribution to Book

Publication Date

10-2022

Publication Title

Adaptation, Learning, and Optimization

Volume

27

First Page

29

Last Page

55

Abstract

The advent of federated learning has facilitated large-scale data exchange amongst machine learning models while maintaining privacy. Despite its brief history, federated learning is rapidly evolving to make wider use more practical. One of the most significant advancements in this domain is the incorporation of transfer learning into federated learning, which overcomes fundamental constraints of primary federated learning, particularly in terms of security. This chapter performs a comprehensive survey on the intersection of federated and transfer learning from a security point of view. The main goal of this study is to uncover potential vulnerabilities and defense mechanisms that might compromise the privacy and performance of systems that use federated and transfer learning.

DOI

10.1007/978-3-031-11748-0_3

ISSN

1867-4534

E-ISSN

1867-4542

ISBN

978-3-031-11747-3

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