Flute: Scalable Federated Learning Simulation

New to me, of interest.  Considering use. 

FLUTE: A scalable federated learning simulation platform   From Microsoft Labs

Published May 16, 2022

By Dimitrios Dimitriadis , Principal Researcher  Mirian Hipolito Garcia , Research Software Engineer  Daniel Eduardo Madrigal Diaz , Senior Research Software Engineering  Andre Manoel , Senior Research Software Engineer  Robert Sim , Principal Research Manager

Federated learning has become a major area of machine learning (ML) research in recent years due to its versatility in training complex models over massive amounts of data without the need to share that data with a centralized entity. However, despite this flexibility and the amount of research already conducted, it’s difficult to implement due to its many moving parts—a significant deviation from traditional ML pipelines.

The challenges in working with federated learning result from the diversity of local data and end-node hardware, privacy concerns, and optimization constraints. These are compounded by the sheer volume of federated learning clients and their data and necessitates a wide skill set, significant interdisciplinary research efforts, and major engineering resources to manage. In addition, federated learning applications often need to scale the learning process to millions of clients to simulate a real-world environment. All of these challenges underscore the need for a simulation platform, one that enables researchers and developers to perform proof-of-concept implementations and validate performance before building and deploying their ML models. 

A versatile framework for federated learning

Today, the Privacy in AI team at Microsoft Research is thrilled to introduce Federated Learning Utilities and Tools for Experimentation (FLUTE) as a framework for running large-scale offline federated learning simulations, which we discuss in detail in the paper, “FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations.” In creating FLUTE, our goal was to develop a high-performance simulation platform that enables quick prototyping of federated learning research and makes it easier to implement federated learning applications. .... '