FLSim: An Extensible and Reusable Simulation Framework for Federated Learning

Li Li, Jun Wang, Chengzhong Xu

Abstract: Federated learning is designed for multiple mobile devices to collaboratively train an artificial intelligence model while preserving data privacy. Instead of collecting the raw training data from mobile devices to the central server, federated learning coordinates a group of devices to train a shared model in a distributed manner with their local data. However, prior to effectively deploying federated learning on resource-constrained mobile devices in large scale, different factors including the convergence rate, energy efficiency and model accuracy should be well studied. Thus, a flexible simulation framework that can be used to investigate a wide range of problems related to federated learning is urgently required.