Federated Learning Approaches for Privacy-Preserving AI in Cloud

Abstract

As artificial intelligence (AI) continues to advance, concerns over data privacy and security have become paramount. Federated learning emerges as a promising paradigm to address these concerns by enabling collaborative model training across distributed devices while preserving data privacy. This paper explores various federated learning approaches for privacy-preserving AI in cloud environments. We delve into the concepts, methodologies, challenges, and future directions of federated learning, emphasizing its significance in ensuring privacy in AI applications deployed on cloud platforms.