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Federated Learning (FL) is the privacy-preserving machine learning paradigmwhich collaboratively trains a model across millions of devices. Simulatedenvironments are fundamental to large-scale FL research, allowing researchersto quickly test new ideas to solve system and statistical heterogeneity issues.This work proposes \emph{Pollen}, a novel resource-aware system capable ofspeeding up FL simulations by efficiently placing clients across distributedand heterogeneous hardware. We propose minimising server-GPU communication andusing an efficient client placement policy based on the inherent trade-offs ofFL client placement on heterogeneous GPUs. These trade-offs are exploredexperimentally. This exploration has been conducted via relevant baselines onthree popular FL tasks: image classification, speech recognition and textgeneration. We compare \emph{Pollen} to existing ad-hoc FL frameworks, such asFlower, Flute and FedScale, and show performance gains of $50\%$ to $400\%$.

High-throughput Simulation of Federated Learning via Resource-Aware Client Placement