Federated Learning for Secure and Resilient AI Systems

Abstract

Federated Learning is a transformative approach to building secure and resilient AI systems by enabling decentralized model training without exposing raw data. As part of Challenges and Solutions for Cybersecurity and Adversarial Machine Learning, this chapter examines its role in enhancing cybersecurity and mitigating adversarial threats, emphasizing its privacy-preserving capabilities and robustness against attacks. Key security challenges, including adversarial model poisoning, communication risks, and data privacy concerns, are analyzed alongside solutions such as differential privacy, secure aggregation, and robust optimization techniques. The discussion extends to Federated Learning's applications in critical sectors such as healthcare, finance, and edge computing, where secure AI deployment is essential. Addressing these challenges and proposing viable solutions, the chapter provides a comprehensive perspective on Federated Learning's potential to enhance AI security and resilience in adversarial environments.