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 × faster wall-clock time and 2.6× better GPU utilisation, which enables large-scale experiments on heterogeneous clients.
@article{arxiv.2207.01053,
title = {Protea: Client Profiling within Federated Systems using Flower},
author = {Wanru Zhao and Xinchi Qiu and Javier Fernandez-Marques and Pedro P. B. de Gusmão and Nicholas D. Lane},
journal= {arXiv preprint arXiv:2207.01053},
year = {2022}
}