Privacy-Preserving AI Through Federated Learning

Abstract

Federated Learning (FL) is revolutionizing the landscape of decentralized machine learning by enabling collaborative model training across multiple devices without the need to centralize data. This paper provides a comprehensive exploration of federated learning as a privacy-preserving technique in artificial intelligence (AI), examining critical challenges such as data security, communication efficiency, and inference attacks. This paper focuses on robust solutions including differential privacy, homomorphic encryption, and federated optimization to enhance the effectiveness of FL. Potential future directions for the application of federated learning in sensitive domains, demonstrating its promise for secure and efficient AI systems are additionally discussed.