Federated Machine Learning (FL) has received considerable attention in recent years. FL benchmarks are predominantly explored in either simulated systems or data center environments, neglecting the setups of real-world systems, which are often closely linked to edge computing. We close this research gap by introducing FLEdge, a benchmark targeting FL workloads in edge computing systems. We systematically study hardware heterogeneity, energy efficiency during training, and the effect of various differential privacy levels on training in FL systems. To make this benchmark applicable to real-world scenarios, we evaluate the impact of client dropouts on state-of-the-art FL strategies with failure rates as high as 50%. FLEdge provides new insights, such as that training state-of-the-art FL workloads on older GPU-accelerated embedded devices is up to 3x more energy efficient than on modern server-grade GPUs.

FLEdge是一个针对边缘计算系统中FL工作负载的基准测试，通过系统地研究硬件异构、训练期间的能量效率以及不同差分隐私级别对FL系统的训练效果的影响，检验了客户端退货率对最先进的FL策略的影响，发现在老GPU加速嵌入式设备上训练现代的FL工作负载比在现代服务器级别的GPU上训练要节省能源高达3倍。

FLEdge：边缘计算系统中联邦机器学习应用的基准测试