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.

翻译：自2016年提出以来，联邦学习（FL）已从基础算法发展为针对多样化挑战和用例的复杂方法学。然而，针对大量现有先进解决方案进行新颖FL技术的研究与基准测试仍具挑战性。为简化此过程，我们引入了FLsim——一个全面的FL仿真框架，旨在满足文献中FL工作流的多样化需求。FLsim以其模块化、可扩展性、资源效率以及实验结果的可控复现性为特点。其易用接口允许用户通过作业配置指定定制化的FL需求，该配置支持：(a) 从非独立同分布数据到独立同分布数据的定制化数据分布，(b) 根据用户偏好选择本地学习算法，且完全独立于任何特定机器学习库，(c) 选择描述节点间通信模式的网络拓扑结构，(d) 定义模型聚合与共识算法，以及(e) 可插拔的区块链支持以增强鲁棒性。通过一系列实验评估，我们证明了FLsim在仿真各类先进FL实验方面的有效性与多功能性。我们预计FLsim将成为FL仿真框架领域的重大进展，为研究者和实践者提供前所未有的灵活性与功能性。