开源的模块化联邦学习框架，支持多种实验场景与算法。
FedModule: A Modular Federated Learning Framework
- 模块化设计拆分联邦学习流程，可灵活组合不同范式。
- 支持同步、异步及个性化学习，内置20+算法。
- 适合需要多场景测试和性能分析的研究者使用。
联邦学习（FL）已广泛应用于医疗、金融和智慧城市等领域。然而，随着实验场景日益复杂，现有FL框架与基准测试难以跟上发展。本文提出FedModule，一个灵活可扩展的开源FL实验框架，旨在支持多样化的FL范式，并为复杂场景提供全面的基准测试。该框架遵循“一代码，多场景”原则，采用模块化设计，将FL流程分解为独立组件，实现不同范式的无缝集成。支持同步、异步及个性化联邦学习，包含20余种已实现算法。在公开数据集上的实验表明其灵活性与可扩展性。框架提供线性、线程、进程和分布式等多种执行模式，用户可根据需求定制环境。同时具备完善的日志记录与测试功能，便于对算法性能进行深入分析。与TensorFlow Federated、PySyft、Flower和FLGo等现有工具包对比，FedModule在可扩展性、灵活性和基准支持方面表现更优。通过解决当前框架的局限性，FedModule显著推动了联邦学习实验的发展，为研究人员和实践者提供了强大的工具，适用于多种实验场景下的算法开发与评估。
原文摘要 · Abstract (English)
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.
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