FLSim: An Extensible and Reusable Simulation Framework for Federated Learning

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.

In this paper, we propose FLSim, a framework for efficiently building simulators for federated learning. Unlike ad hoc simulators, FLSim is envisioned as an open repository of building blocks for creating simulators. To this end, FLSim consists of a set of software components organized in a well-structured software architecture that provides the foundation for maximizing flexibility and extensibility. With FLSim, creating a simulator generally involves only putting the selected components together, thus allowing users to focus on the problems being studied. We describe the design of the framework in detail and use a few use cases to demonstrate the ease with which various simulators can be constructed with FLSim.