Enhancing Privacy and Utility in Federated Learning: A Hybrid P2P and Server-Based Approach with Differential Privacy Protection

Luca Corbucci, Anna Monreale, Roberto Pellungrini

Abstract: Federated Learning has been recently adopted in several contexts as a solution to train a Machine Learning model while preserving users’ privacy. Even though it avoids data sharing among the users involved in the training, it is common to use it in conjunction with a privacy-preserving technique like DP due to potential privacy issues. Unfortunately, often the application of privacy protection strategies leads to a degradation of the model’s performance. Therefore, in this paper, we propose a framework that allows the training of a collective model through Federated Learning using a hybrid architecture that enables clients to mix within the same learning process collaborations with (semi-)trusted entities and collaboration with untrusted participants. To reach this goal we design and develop a process that exploits both the classic Client-Server and the Peer-to-Peer training mechanism. To evaluate how our methodology could impact the model utility we present an experimental analysis