Demo: A Practical Testbed for Decentralized Federated Learning on Physical Edge Devices

TL;DR

This paper presents a practical physical testbed for decentralized federated learning on edge devices, demonstrating how topology affects model performance and energy consumption in real-world scenarios.

Contribution

It introduces a deployable physical testbed for DFL on resource-constrained devices, extending NEBULA with energy monitoring and analyzing topology effects.

Findings

Denser communication topologies improve model accuracy.

Energy consumption varies with topology and training duration.

The testbed enables real-world evaluation of DFL on edge hardware.

Abstract

Federated Learning (FL) enables collaborative model training without sharing raw data, preserving participant privacy. Decentralized FL (DFL) eliminates reliance on a central server, mitigating the single point of failure inherent in the traditional FL paradigm, while introducing deployment challenges on resource-constrained devices. To evaluate real-world applicability, this work designs and deploys a physical testbed using edge devices such as Raspberry Pi and Jetson Nano. The testbed is built upon a DFL training platform, NEBULA, and extends it with a power monitoring module to measure energy consumption during training. Experiments across multiple datasets show that model performance is influenced by the communication topology, with denser topologies leading to better outcomes in DFL settings.