Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms
It addresses security concerns for researchers and practitioners using federated and transfer learning, but is incremental as it synthesizes existing knowledge rather than presenting new findings.
This survey examines the intersection of federated and transfer learning, focusing on identifying vulnerabilities and defense mechanisms that could compromise privacy and performance in these systems.
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.