Design, Implementation, and Analysis of a Federated Learning Architecture

Abstract

Artificial Intelligence (AI) has become the go-to technology for many applications. This method, even having huge advantages in many areas, also require from training step, which is very power-and-time consuming and has some privacy concerns. As a consequence, distributed training techniques are emerging, such as Federated Learning (FL), because it does not only enables to distribute the computational load but training sensible data locally without the necessity of sending it to a main server, for privacy reasons. In this thesis, we present a framework that achieves exactly this, tested with tactile data, which as far as we are aware, has never been tried before. The framework is lightweight and uses encrypted HTTP protocol to exchange information between the different parties. It is benchmarked in different scenarios, such as same-server and multiple-server configurations, and some results show how it can be superior/faster to the centralized scenario, with the plus of more robust security.