Demo：面向物理边缘设备的去中心化联邦学习实用测试平台
Demo: A Practical Testbed for Decentralized Federated Learning on Physical Edge Devices
AI总结：
本文设计并部署基于 Raspberry Pi、Jetson Nano 与 NEBULA 的去中心化联邦学习物理测试平台，加入功耗监测以评估真实边缘部署，并通过多数据集实验揭示通信拓扑密度对模型性能的影响。
摘要：
联邦学习（FL）能够在不共享原始数据的情况下实现协同模型训练，从而保护参与者隐私。去中心化联邦学习（DFL）消除了对中央服务器的依赖，缓解了传统 FL 范式固有的单点故障问题，但同时也给资源受限设备上的部署带来了挑战。为评估真实世界的适用性，本工作设计并部署了一个使用 Raspberry Pi 和 Jetson Nano 等边缘设备的物理测试平台。该测试平台构建在 DFL 训练平台 NEBULA 之上，并通过功耗监测模块对其进行扩展，以测量训练期间的能耗。跨多个数据集的实验表明，模型性能会受到通信拓扑的影响，在 DFL 设置中，更密集的拓扑会带来更好的结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
