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arXiv · 2504.04678

Beyond Assumptions: Measuring Federated Learning over Real 5G Networks

Robert J. Hayek·Kayla Comer·Joaquin Chung·Chandra R. Murthy·Rajkumar Kettimuthu·Igor Kadota
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Abstract

Deploying FL using IoT devices is an area poised to significantly benefit from advances in NextG wireless. In this paper, we deploy a FL application using a 5G-NR Standalone (SA) testbed with open-source and Commercial Off-the-Shelf (COTS) components. The 5G testbed architecture consists of a network of resource-constrained edge devices, namely Raspberry Pis, and a central server equipped with a Software Defined Radio (SDR) and running O-RAN software. Our testbed allows edge devices to communicate with the server using WiFi and Ethernet in addition to 5G. FL is deployed using the Flower FL framework, extended with custom instrumentation for communication and ML metrics. We analyze the FL application across three network interfaces--5G, WiFi, and Ethernet--as well as across 5G bandwidths and uplink-downlink scheduling ratios. Our experimental results challenge some common assumptions about communication time in FL over wireless and discuss the potential pitfalls of these assumptions. We find that there is a consistent straggler in about 70% of trials, while in the other 30%, high communication time causes competing stragglers. We also compare FL performance over 5G with and without external congestion and compare our testbed to commercial 5G to validate our findings in a broader context. For reproducibility, we have open-sourced our FL application, instrumentation tools, and testbed configuration.

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Robert J. Hayek, Kayla Comer, Joaquin Chung, Chandra R. Murthy, Rajkumar Kettimuthu, Igor Kadota. 2025-04-07. Beyond Assumptions: Measuring Federated Learning over Real 5G Networks. https://arxiv.org/abs/2504.04678

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