Microsoft Research has recently released Federated Learning Utilities and Tools for Experimentation (FLUTE), a new simulation framework to accelerate federated learning ML algorithm development. It opens a new direction in federated learning algorithm design and experimentation by allowing engineers and researchers to design and simulate new algorithms before development and deployment.
FLUTE is a simulation framework for running large-scale offline federated learning algorithms. The main goal of federated learning is to train complex machine-learning models over massive amounts of data without the need to share that data in a centralized location. In this approach, the initial global model is uploaded on each device with limited computational power capacity.
