Federated Learning for Privacy - Preserving Collaborative AI: Exploring federated learning techniques for training AI models collaboratively while preserving data privacy

Abstract

Federated learning (FL) has emerged as a promising approach for collaborative model training across decentralized devices while maintaining data privacy. This paper provides a comprehensive overview of FL techniques, highlighting its advantages, challenges, and applications in privacy-preserving collaborative AI. We discuss the federated learning process, including client-server communication, model aggregation, and privacy-preserving mechanisms. Additionally, we review recent advancements and future research directions in FL for privacy-preserving collaborative AI. The paper concludes with a discussion on the potential impact of FL on the future of AI and data privacy.