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.
Top1. Introduction
In the era of rapid technological advancements, the convergence of diverse technologies has unlocked novel possibilities for revolutionizing various industries. This chapter delves into the synergistic relationship between two cutting-edge paradigms like Federated Learning (FL) and In-Memory Computing (IMC). By amalgamating these two innovative concepts, this research explores their integration within the context of drone systems, envisioning a transformative approach to enhancing mission efficiency, data security, and real-time analytics capabilities(Jeon et al., 2020).
Background and Motivation: Federated Learning has emerged as a ground-breaking approach in the domain of machine learning. It addresses the challenges of centralized model training by allowing devices at the edge of the network, such as smartphones and IoT devices, to collaboratively train models while keeping their data localized. This paradigm is particularly suited for scenarios where data privacy is paramount, as it enables model updates without the need to transmit raw data(Hema et al., 2023; Karthik et al., 2023; Koshariya et al., 2023). Concurrently, In-Memory Computing leverages the rapid data processing capabilities of memory, unlocking the potential for real-time analytics and decision-making. The integration of these technologies offers the prospect of enhancing drone systems' capabilities, spanning industries such as agriculture, surveillance, disaster management, and logistic(Abualigah et al., 2021; Angjo et al., 2021)s.
1.1 Research Objectives
The primary objective is to investigate the integration of Federated Learning and In-Memory Computing within drone systems. By developing an experimental framework, the potential enhancements in mission efficiency, data security, and real-time data processing are illustrated. The interplay of these technologies holds the promise of transforming drones from data collection devices to intelligent decision-making platforms.
1.2 Scope of the Study
This chapter focuses on the design, implementation, and evaluation of a novel integration framework that combines Federated Learning and In-Memory Computing. The scope encompasses both the technical aspects of the integration process and the practical implications it holds for drone missions. The study evaluates the performance gains achieved through this integration in terms of mission completion time, data privacy preservation, and processing speed.
Organization: This chapter reviews the literature on Federated Learning and In-Memory Computing, their applications in drone technology, and their theoretical framework. It then discusses experimental design, hardware specifications, and data collection procedures. The integration framework is developed, and experimental results are presented, showcasing the benefits of the synergy between Federated Learning and In-Memory Computing. The chapter concludes with a comprehensive conclusion, highlighting the implications, challenges, and potential future directions of this integration.