搭建真实边缘设备测试平台，验证去中心化联邦学习的可行性与能耗表现。
Demo: A Practical Testbed for Decentralized Federated Learning on Physical Edge Devices
- 基于NEBULA平台构建物理测试床，集成电源监控模块。
- 通信拓扑越密集，去中心化训练效果越好，性能提升显著。
- 适合关注边缘计算与隐私保护的开发者与研究者。
联邦学习（FL）可在不共享原始数据的情况下实现协同模型训练，保护参与者隐私。去中心化联邦学习（DFL）摆脱对中心服务器的依赖，避免传统FL架构中的单点故障问题，但在资源受限的设备上部署面临挑战。为评估实际应用潜力，本文设计并部署了一个基于树莓派和Jetson Nano等边缘设备的物理测试平台。该平台建立在DFL训练框架NEBULA之上，并扩展了电源监控模块以测量训练过程中的能耗。多组实验在多个数据集上进行，结果表明，在去中心化设置下，模型性能受通信拓扑影响，拓扑越密集，训练效果越好。
原文摘要 · Abstract (English)
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.
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