FedModule: A Modular Federated Learning Framework

TL;DR

FedModule is a modular, extensible federated learning framework that supports diverse paradigms, multiple algorithms, and comprehensive benchmarking, facilitating complex experimental setups and detailed performance analysis.

Contribution

Introduces FedModule, a flexible FL framework with a modular design supporting various paradigms, algorithms, and execution modes, addressing limitations of existing tools.

Findings

Demonstrates flexibility and extensibility through experiments on public datasets.

Supports over 20 FL algorithms across different paradigms.

Outperforms existing FL toolkits in scalability and benchmarking capabilities.

Abstract

Federated learning (FL) has been widely adopted across various applications, such as healthcare, finance, and smart cities. However, as experimental scenarios become more complex, existing FL frameworks and benchmarks have struggled to keep pace. This paper introduces FedModule, a flexible and extensible FL experimental framework that has been open-sourced to support diverse FL paradigms and provide comprehensive benchmarks for complex experimental scenarios. FedModule adheres to the "one code, all scenarios" principle and employs a modular design that breaks the FL process into individual components, allowing for the seamless integration of different FL paradigms. The framework supports synchronous, asynchronous, and personalized federated learning, with over 20 implemented algorithms. Experiments conducted on public datasets demonstrate the flexibility and extensibility of FedModule. The framework offers multiple execution modes-including linear, threaded, process-based, and distributed-enabling users to tailor their setups to various experimental needs. Additionally, FedModule provides extensive logging and testing capabilities, which facilitate detailed performance analysis of FL algorithms. Comparative evaluations against existing FL toolkits, such as TensorFlow Federated, PySyft, Flower, and FLGo, highlight FedModule's superior scalability, flexibility, and comprehensive benchmark support. By addressing the limitations of current FL frameworks, FedModule marks a significant advancement in FL experimentation, providing researchers and practitioners with a robust tool for developing and evaluating FL algorithms across a wide range of scenarios.