FedML: A Research Library and Benchmark for Federated Machine Learning
This provides a standardized tool for the federated learning research community to improve reproducibility and comparison, though it is incremental as it builds on existing FL concepts.
The authors tackled the lack of support for diverse algorithmic development and fair comparison in federated learning by introducing FedML, an open research library and benchmark that facilitates algorithm development and performance evaluation across three computing paradigms.
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.