  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 limiting factors from existing simulators:
(a) communication inefficiency derived from pull-based client execution and (b)
inadequate load balance when using heterogeneous hardware. Pollen executes
high-throughput FL simulations at scale by (a) using a push-based client
placement system, (b) learning how an adaptable scheduling of clients based on
hardware statistics (c) estimating the optimal number of concurrent workers per
GPU. We evaluate Pollen on four representative FL tasks and show that Pollen's
placement model increases GPU utilization and reduces idle time. We compare
Pollen to Flower, Flute, FedScale, Parrot, and pfl and show experimental
speed-ups of days or weeks.
