Federated learning (FL) has found numerous applications in healthcare, finance, and IoT scenarios. Many existing FL frameworks offer a range of benchmarks to evaluate the performance of FL under realistic conditions. However, the process of customizing simulations to accommodate application-specific settings, data heterogeneity, and system heterogeneity typically remains unnecessarily complicated. This creates significant hurdles for traditional ML researchers in exploring the usage of FL, while also compromising the shareability of codes across FL frameworks. To address this issue, we propose a novel lightweight FL platform called FLGo, to facilitate cross-application FL studies with a high degree of shareability. Our platform offers 40+ benchmarks, 20+ algorithms, and 2 system simulators as out-of-the-box plugins. We also provide user-friendly APIs for quickly customizing new plugins that can be readily shared and reused for improved reproducibility. Finally, we develop a range of experimental tools, including parallel acceleration, experiment tracker and analyzer, and parameters auto-tuning. FLGo is maintained at \url{flgo-xmu.github.io}.

翻译：联邦学习已在医疗、金融和物联网场景中得到广泛应用。现有诸多联邦学习框架提供了丰富的基准测试，用于评估联邦学习在现实条件下的性能。然而，针对应用特定场景、数据异质性和系统异质性进行定制化仿真的过程往往过于复杂。这为传统机器学习研究者探索联邦学习的应用设置了重大障碍，同时也影响了联邦学习框架间代码的可共享性。为解决这一问题，我们提出一个名为FLGo的新型轻量级联邦学习平台，旨在以高度可共享性促进跨应用联邦学习研究。该平台提供40余项基准测试、20余种算法及2个系统模拟器作为即用型插件。我们还提供用户友好的API，用于快速定制可便捷共享与复用的新插件，以提升可重复性。最后，我们开发了包括并行加速、实验跟踪分析器和参数自动调优在内的一系列实验工具。FLGo的维护地址为\url{flgo-xmu.github.io}。