Optimizing Energy in Federated Learning over Low-Power and High Packet Loss Networks

• Johann J. Schmitz Bastos UFES http://orcid.org/0000-0002-2763-7162
• João Pedro C. Batista UFES https://orcid.org/0009-0006-8300-8236
• Ramon dos Reis Fontes UFRN https://orcid.org/0000-0003-0012-4284
• Eduardo Cerqueira UFPA https://orcid.org/0000-0003-2162-6523
• Rodolfo S. Villaça UFES http://orcid.org/0000-0002-8051-3978
• Vinícius F. S. Mota UFES https://orcid.org/0000-0002-8341-8183

DOI: https://doi.org/10.5753/sbrc.2025.5786

Abstract

Federated Learning (FL) enables the distributed training of machine learning models without the need to share raw data. In IoT networks characterized by resource-constrained devices and high packet loss, selecting devices for training presents significant challenges. This work proposes optimizing the selection process by focusing on the energy efficiency of devices utilizing the RPL protocol, a protocol that builds a tree like topology of the network. The proposed approach was evaluated through an extension of the MininetFed platform, incorporating support for RPL networks, energy consumption monitoring, and a selection algorithm designed to extend the lifetime of IoT networks during training without compromising effectiveness. The proposed approach achieved a reduction of circa 6,5% in the energy consumption of central nodes.

Keywords: Federated Learning, IoT Networks, Energy Efficiency, RPL (Routing Protocol for Low-Power and Lossy Networks), Device Selection, MininetFed

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