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FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations
Mirian Hipolito Garcia · Andre Manoel · Daniel Madrigal · Robert Sim · Dimitrios Dimitriadis
Event URL: https://openreview.net/forum?id=KakQPQbginE »

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 we 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 up to 42x and savings in memory footprint of 3x. A sample of the platform capabilities is presented in the Appendix for a range of tasks and other functionality such as scaling and a variety of federated optimizers.

Author Information

Mirian Hipolito Garcia (Research, Microsoft)

Mirian Hipólito holds a Bachelor’s degree in Digital Systems and Robotics Engineering from Tecnológico de Monterrey in 2020. She previously worked in the automotive sector, gaining experience in the fields of autonomous vehicles, robotics and automation. She currently works as a Research Software engineer in MSR as part of Privacy in Artificial Intelligence Team.

Andre Manoel (OWKIN)
Daniel Madrigal
Robert Sim (Microsoft Corporation)
Dimitrios Dimitriadis (Microsoft Research)

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