Radio Resource Scheduling to Support Federated Learning in 5G Networks

• Renan R. de Oliveira UFG / IFG
• Luan Gabriel S. Oliveira UFG
• Carlos Eduardo da S. Santos IFTO
• Kleber V. Cardoso UFG
• Antonio Oliveira-Jr UFG / Fraunhofer Portugal AICOS

DOI: https://doi.org/10.5753/sbrc.2026.19200

Abstract

This article proposes a scheduling algorithm for Federated Learning tasks in 5G networks under non-priority concurrent background traffic and constraints on communication resources. The algorithm selects devices with more representative data and better channel conditions, employs a scheduling heuristic that prioritizes model-update flows while ensuring coexistence with background traffic, and applies an aggregation strategy that reinforces the contributions that promote convergence of the global model. The implementation is integrated into the 5G-LENA module of ns-3 and demonstrates improvements in convergence and in key performance indicators of the 5G network, surpassing traditional scheduling algorithms.

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