In this paper we introduce "Federated Learning Utilities and Tools for Experimentation" (FLUTE), a high-performance open-source platform for federated learning research and offline simulations. The goal of FLUTE is to enable rapid prototyping and simulation of new federated learning algorithms at scale, including novel optimization, privacy, and communications strategies. We describe the architecture of FLUTE, enabling arbitrary federated modeling schemes to be realized. We compare the platform with other state-of-the-art platforms and describe available features of FLUTE for experimentation in core areas of active research, such as optimization, privacy, and scalability. A comparison with other established platforms shows speed-ups of up to 42x and savings in memory footprint of 3x. A sample of the platform capabilities is also presented for a range of tasks, as well as other functionality, such as linear scaling for the number of participating clients, and a variety of federated optimizers, including FedAdam, DGA, etcetera.
@article{arxiv.2203.13789,
title = {FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations},
author = {Mirian Hipolito Garcia and Andre Manoel and Daniel Madrigal Diaz and Fatemehsadat Mireshghallah and Robert Sim and Dimitrios Dimitriadis},
journal= {arXiv preprint arXiv:2203.13789},
year = {2022}
}