Constructing Executing and Overcoming Challenges in Distributed AI Systems: A Study of Federated Learning Framework

Abstract

Federated learning stands out as a promising approach within the realm of distributed artificial intelligence (AI) systems, facilitating collaborative model training across decentralized devices while safeguarding data privacy. This study presents a thorough investigation into federated learning architecture, covering its foundational design principles, implementation methodologies, and the significant challenges encountered in distributed AI systems. We delve into the fundamental mechanisms underpinning federated learning, elucidating its merits in diverse environments and its prospective applications across various domains. Additionally, we scrutinize the technical complexities associated with deploying federated learning systems, including considerations such as communication efficiency, model aggregation techniques, and security protocols. By amalgamating insights gleaned from recent research endeavors and practical deployments, this study furnishes valuable guidance for both researchers and practitioners aiming to harness federated learning for the development of scalable and privacy-preserving AI solutions.