Abstract
One of the main lines of research in distributed learning in the last years is the one related to Federated Learning (FL). In this work, a decentralized Federated Learning algorithm based on consensus (CoL) is applied to Wireless Ad-hoc Networks (WANET), where the agents communicate with other agents to share their learning model as they are available to the range of the wireless connection. When deploying a set of agents is very important to study previous to the deployment if all the agents in the WANET will be reachable. The paper proposes to study it by generating a simulation close to the real world using a framework that allows the easy development and modification of simulations based on Unity and SPADE agents. A fruit orchard with autonomous tractors is presented as a case study.
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Acknowledgements
This work has been developed thanks to the funding of projects: Grant PID2021-123673OB-C31 funded by MCIN/AEI/ 10.13039/ 501100011033 and by “ERDF A way of making Europe”, PROMETEO CIPROM/ 2021/077, TED2021-131295B-C32 and Ayudas del Vicerrectorado de Investigacion de la UPV (PAID-PD-22).
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Rebollo, M., Rincon, J.A., Hernández, L., Enguix, F., Carrascosa, C. (2023). GTG-CoL: A New Decentralized Federated Learning Based on Consensus for Dynamic Networks. In: Mathieu, P., Dignum, F., Novais, P., De la Prieta, F. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics. The PAAMS Collection. PAAMS 2023. Lecture Notes in Computer Science(), vol 13955. Springer, Cham. https://doi.org/10.1007/978-3-031-37616-0_24
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