Latency-Energy Tradeoffs in Federated Learning on Resource Constrained Edge Computing Systems

Artificial intelligence and machine learning have become of crucial importance in many scientific and industrial fields, thanks to the ability to extract information, make predictions and identify patterns on data. For the creation of increasingly accurate predictive models, these technologies are based on the collection and control of large amounts of data within controlled systems. Federated learning is a new framework that exploits the computational capabilities and local data of a set of multiple resource-constrained devices coordinated by a central server for the creation of a shared global predictive model, without any centralised data collection. In this work, we focus on assessing the performance of federated learning executed on resource constrained Edge computing system. A set of experiments to assess the energy consumption and processing times on a set of heterogeneous GPU-enabled embedded systems were executed. Our analysis shows that, by varying the amount of data that each system is in charge of processing, it is possible to identify a trade-off between the overall energy consumption of the devices and the processing time required to train an effective predictive model.