FLsim让联邦学习实验模拟更灵活高效，支持自定义数据与算法配置。
FLsim: A Modular and Library-Agnostic Simulation Framework for Federated Learning
- 模块化设计，可自由配置数据分布、本地算法和通信拓扑。
- 支持非独立同分布与独立同分布数据，兼容多种学习算法。
- 适合研究者快速搭建实验，尤其适合复杂联邦学习场景测试。
联邦学习自2016年提出以来，已从基础算法演进至针对多样挑战的复杂方法。然而，对新方法与大量现有先进方案进行对比研究仍具挑战。为此，我们提出FLsim，一个全面的联邦学习仿真框架，旨在满足文献中多样的联邦学习工作流需求。FLsim具备模块化、可扩展性、资源高效及实验结果可控复现的特点。其易用接口允许用户通过任务配置指定定制化需求，包括：(a) 从非独立同分布（non-iid）到独立同分布（iid）的数据分布，(b) 按需选择本地学习算法，完全与机器学习库无关，(c) 选取节点间通信模式的网络拓扑，(d) 定义模型聚合与共识算法，(e) 可插拔区块链支持以增强鲁棒性。通过一系列实验评估，我们验证了FLsim在模拟多种先进联邦学习实验中的有效性与通用性。预计FLsim将显著推动联邦学习仿真框架的发展，为研究人员与实践者提供前所未有的灵活性与功能支持。
原文摘要 · Abstract (English)
Federated Learning (FL) has undergone significant development since its inception in 2016, advancing from basic algorithms to complex methodologies tailored to address diverse challenges and use cases. However, research and benchmarking of novel FL techniques against a plethora of established state-of-the-art solutions remain challenging. To streamline this process, we introduce FLsim, a comprehensive FL simulation framework designed to meet the diverse requirements of FL workflows in the literature. FLsim is characterized by its modularity, scalability, resource efficiency, and controlled reproducibility of experimental outcomes. Its easy to use interface allows users to specify customized FL requirements through job configuration, which supports: (a) customized data distributions, ranging from non-independent and identically distributed (non-iid) data to independent and identically distributed (iid) data, (b) selection of local learning algorithms according to user preferences, with complete agnosticism to ML libraries, (c) choice of network topology illustrating communication patterns among nodes, (d) definition of model aggregation and consensus algorithms, and (e) pluggable blockchain support for enhanced robustness. Through a series of experimental evaluations, we demonstrate the effectiveness and versatility of FLsim in simulating a diverse range of state-of-the-art FL experiments. We envisage that FLsim would mark a significant advancement in FL simulation frameworks, offering unprecedented flexibility and functionality for researchers and practitioners alike.
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