标题： FLsim：联邦学习的模块化和库无关的仿真框架 显示英文标题

标题： FLsim: A Modular and Library-Agnostic Simulation Framework for Federated Learning

摘要： 联邦学习（FL）自2016年出现以来经历了显著的发展，从基本算法进步到针对解决各种挑战和用例的复杂方法。 然而，将新的FL技术与大量已有的最先进的解决方案进行研究和基准测试仍然具有挑战性。 为了简化这一过程，我们引入了FLsim，这是一个全面的FL仿真框架，旨在满足文献中FL工作流的多样化需求。 FLsim的特点是模块化、可扩展性、资源效率以及实验结果的可控可重复性。 其易于使用的界面允许用户通过作业配置指定自定义的FL需求，这包括：(a) 自定义的数据分布，从非独立同分布（non-iid）数据到独立同分布（iid）数据，(b) 根据用户偏好选择本地学习算法，对ML库完全无差别，(c) 选择展示节点之间通信模式的网络拓扑，(d) 定义模型聚合和共识算法，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，我们展示了FLsim在模拟各种先进的FL实验中的有效性和多样性。 我们预计， FLsim将在FL仿真框架中取得重大进展，为研究人员和实践者提供前所未有的灵活性和功能。 显示英文摘要

摘要： 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.