Human Activity Recognition (HAR) is a classification problem that aims to discern well-defined human activities through sensor data. This thesis explores HAR with a focus on privacy concerns by adopting the PHAR architecture. The architecture adopts a decentralized approach, moving the primary computational effort to the data. Additionally, the purpose of the project is to illustrate how nowadays smartphone devices can be used to collect and train on models their own sensor data. The architecture for this project is built on top of a Flower framework with Android devices connected to a server that performs Federated Learning (FL) of convolutional neural network (CNN) exploiting federated averaging (FedAvg).