Flower: A Friendly Federated Learning Research Framework

TL;DR

Flower is a versatile federated learning framework that enables large-scale experiments on heterogeneous edge devices, bridging simulation and real-world deployment for FL research.

Contribution

It introduces a scalable FL framework supporting large client populations and heterogeneous devices, facilitating both simulation and real-device experiments.

Findings

Supports FL experiments with up to 15 million clients using minimal hardware

Enables seamless transition from simulation to real device deployment

Provides a comprehensive tool for FL research and development

Abstract

Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared prediction model, while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store the data in the cloud. However, FL is difficult to implement realistically, both in terms of scale and systems heterogeneity. Although there are a number of research frameworks available to simulate FL algorithms, they do not support the study of scalable FL workloads on heterogeneous edge devices. In this paper, we present Flower -- a comprehensive FL framework that distinguishes itself from existing platforms by offering new facilities to execute large-scale FL experiments and consider richly heterogeneous FL device scenarios. Our experiments show Flower can perform FL experiments up to 15M in client size using only a pair of high-end GPUs. Researchers can then seamlessly migrate experiments to real devices to examine other parts of the design space. We believe Flower provides the community with a critical new tool for FL study and development.