Exploring Threats, Defenses, and Privacy-Preserving Techniques in Federated Learning: A Survey

• Published in: Computer (Long Beach, Calif.) Vol. 57; no. 4; pp. 46 - 56
• Main Authors: Huang, Ren-Yi, Samaraweera, Dumindu, Chang, J. Morris
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Federated learning; Robustness (mathematics); Surveys

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.