  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.
