Exploring Threats, Defenses, and Privacy-Preserving Techniques in Federated Learning: A Survey
Ren-Yi Huang, Dumindu Samaraweera, J. Morris Chang
Abstract
This article presents a comprehensive survey of both attack and defense mechanisms within the federated learning (FL) landscape. Furthermore, it explores the challenges involved and outlines future directions for the development of a robust and efficient FL solution.
Topics & Concepts
Internet privacyComputer securityComputer scienceBusinessPrivacy-Preserving Technologies in DataCryptography and Data SecurityAdversarial Robustness in Machine Learning