• distributed learning
• energy-latency tradeoffs
• federated learning
• flower
• jetson

Artificial intelligence (AI) and machine learning (ML) 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 (FL) are new settings that exploit the computational capabilities and local data of a set of devices coordinated by a central server for the creation of a shared global predictive model, without the need for collect data. The objective of this thesis is to implement a distributed infrastructure exploiting Flower, a framework that facilitates the implementation of distributed and federated systems, using two versions of NVIDIA's Jetson as devices, studying and analyzing the characteristics of this infrastructure, and finally design a model that allows to analyze the tradeoffs between system energy consumed and system latency, by varying the amount of data that each device makes available for the development of the global predictive model ML.