Federated Learning (FL) is a privacy-focused machine learning paradigm that collaboratively trains models directly on edge devices. Simulation plays an essential role in FL adoption, helping develop novel aggregation and client sampling strategies. However, current simulators cannot emulate large-scale systems in a time-efficient manner, which limits their utility and casts doubts on generalizability. This work proposes Pollen, a novel resource-aware system for speeding up simulations. Pollen addresses two limitin…