FedML: A Research Library and Benchmark for Federated Machine Learning

TL;DR

FedML is an open-source library and benchmark designed to support diverse federated learning research, enabling fair comparisons and facilitating algorithm development across multiple computing paradigms.

Contribution

FedML introduces a flexible, comprehensive platform with generic APIs and reference implementations to advance federated learning research and evaluation.

Findings

Supports three computing paradigms: on-device, distributed, and simulation.

Provides diverse baseline algorithms, models, and datasets.

Facilitates fair and reproducible FL algorithm comparison.

Abstract

Federated learning (FL) is a rapidly growing research field in machine learning. However, existing FL libraries cannot adequately support diverse algorithmic development; inconsistent dataset and model usage make fair algorithm comparison challenging. In this work, we introduce FedML, an open research library and benchmark to facilitate FL algorithm development and fair performance comparison. FedML supports three computing paradigms: on-device training for edge devices, distributed computing, and single-machine simulation. FedML also promotes diverse algorithmic research with flexible and generic API design and comprehensive reference baseline implementations (optimizer, models, and datasets). We hope FedML could provide an efficient and reproducible means for developing and evaluating FL algorithms that would benefit the FL research community. We maintain the source code, documents, and user community at https://fedml.ai.