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Synergizing Federated Learning and In-Memory Computing: An Experimental Approach for Drone Integration

Synergizing Federated Learning and In-Memory Computing: An Experimental Approach for Drone Integration

J. K. Periasamy, S. Subhashini, M. Mutharasu, M. Revathi, P. Ajitha, Sampath Boopathi
Copyright: © 2024 |Pages: 35
ISBN13: 9798369356432|ISBN13 Softcover: 9798369356449|EISBN13: 9798369356456
DOI: 10.4018/979-8-3693-5643-2.ch004
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MLA

Periasamy, J. K., et al. "Synergizing Federated Learning and In-Memory Computing: An Experimental Approach for Drone Integration." Developments Towards Next Generation Intelligent Systems for Sustainable Development, edited by Shanu Sharma, et al., IGI Global, 2024, pp. 89-123. https://doi.org/10.4018/979-8-3693-5643-2.ch004

APA

Periasamy, J. K., Subhashini, S., Mutharasu, M., Revathi, M., Ajitha, P., & Boopathi, S. (2024). Synergizing Federated Learning and In-Memory Computing: An Experimental Approach for Drone Integration. In S. Sharma, A. Prakash, & V. Sugumaran (Eds.), Developments Towards Next Generation Intelligent Systems for Sustainable Development (pp. 89-123). IGI Global. https://doi.org/10.4018/979-8-3693-5643-2.ch004

Chicago

Periasamy, J. K., et al. "Synergizing Federated Learning and In-Memory Computing: An Experimental Approach for Drone Integration." In Developments Towards Next Generation Intelligent Systems for Sustainable Development, edited by Shanu Sharma, Ayushi Prakash, and Vijayan Sugumaran, 89-123. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-5643-2.ch004

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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.

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