Synergizing Federated Learning and In-Memory Computing

Abstract

This chapter explores the convergence of cutting-edge technologies, namely, federated learning and in-memory computing, through an experimental approach focused on their integration into drone systems. Federated Learning enables collaborative model training across distributed devices while preserving data privacy, making it suitable for scenarios like drone networks. In-Memory computing leverages fast data processing directly in memory, enhancing real-time analytics and decision-making capabilities. This study presents a novel framework that combines these technologies to enhance the performance of drone missions. The architecture, implementation, and experimental setup, demonstrating improved mission efficiency, data security, and processing speed are also described. The results highlight the potential of this synergy in revolutionizing drone applications across various industries.