Federated Learning Architecture: Design, Implementation, and Challenges in Distributed AI Systems

Abstract

Federated learning has emerged as a promising paradigm in the domain of distributed artificial intelligence (AI) systems, enabling collaborative model training across decentralized devices while preserving data privacy. This paper presents a comprehensive exploration of federated learning architecture, encompassing its design principles, implementation strategies, and the key challenges encountered in distributed AI systems. We delve into the underlying mechanisms of federated learning, discussing its advantages in heterogeneous environments and its potential applications across various domains. Furthermore, we analyse the technical intricacies involved in deploying federated learning systems, including communication efficiency, model aggregation techniques, and security considerations. By synthesizing insights from recent research and practical implementations, this paper offers valuable guidance for researchers and practitioners seeking to leverage federated learning in the development of scalable and privacy-preserving AI solutions.