Federated Learning (FL) has emerged as a prospective solution that facilitates the training of a high-performing centralised model without compromising the privacy of users. While successful, research is currently limited by the possibility of establishing a realistic large-scale FL system at the early stages of experimentation. Simulation can help accelerate this process. To facilitate efficient scalable FL simulation of heterogeneous clients, we design and implement Protea, a flexible and lightweight client profiling component within federated systems using the FL framework Flower. It allows automatically collecting system-level statistics and estimating the resources needed for each client, thus running the simulation in a resource-aware fashion. The results show that our design successfully increases parallelism for 1.66 $\times$ faster wall-clock time and 2.6$\times$ better GPU utilisation, which enables large-scale experiments on heterogeneous clients.
翻译:联邦学习联合会(FL)已成为一种前景解决方案,有利于在不损害用户隐私的情况下培训高性能中央化模式,虽然成功,但研究目前受到在试验的早期阶段建立现实的大型FL系统的可能性的限制。模拟可以帮助加快这一进程。为了便利对多种客户进行高效的FL模拟,我们设计和实施Protea,这是使用FL框架Flower的联邦化系统中灵活和轻量化客户特征分析部分。它允许自动收集系统级统计数据并估计每个客户所需的资源,从而以资源意识的方式进行模拟。结果显示,我们的设计成功地增加了1.66美元更快的墙时段和2.6美元更好的GPUP利用的平行性,从而能够对多种客户进行大规模实验。