FedBed: Benchmarking Federated Learning over Virtualized Edge Testbeds

Federated Learning has become the de facto paradigm for training AI models under a distributed modality where the computational effort is spread across several clients without sharing local data. Despite its distributed nature, enabling FL in an Edge-Cloud continuum is challenging with resource and network heterogeneity, different AI models and libraries, and non-uniform data distributions, all hampering QoS and limiting innovation potential. This work introduces FedBed, a testing framework that enables the rapid and reproducible benchmarking of FL deployments on virtualized testbeds. FedBed aids users in assessing the numerous trade-offs that result from combining a variety of FL software and infrastructure configurations in Edge-Cloud settings. This reduces the time-consuming process that includes the setup of either a virtual physical or emulation testbed, experiment configurations, and the monitoring of the resulting FL testbed.