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.
View on arXiv@article{feng2025_2505.08033,
title={ Demo: A Practical Testbed for Decentralized Federated Learning on Physical Edge Devices },
author={ Chao Feng and Nicolas Huber and Alberto Huertas Celdran and Gerome Bovet and Burkhard Stiller },
journal={arXiv preprint arXiv:2505.08033},
year={ 2025 }
}