  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.
