Title
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
Recommended Citation
Hallaji, Ehsan; Razavi-Far, Roozbeh; and Saif, Mehrdad. (2022). Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms. Adaptation, Learning, and Optimization, 27, 29-55.
https://scholar.uwindsor.ca/electricalengpub/186