Using Decentralized Aggregation for Federated Learning with Differential Privacy

On the other hand, although Federated Learning (FL) data silos, eliminating the need for raw data sharing as it has provides some level of privacy by retaining the data at the local the ambition to protect data privacy through distributed learning node, which executes a local training to enrich a global model, this methods that keep the data local. In simple terms, with FL, it is scenario is still susceptible to privacy breaches as membership inference not the data that moves to a model, but it is a model that moves to attacks. To provide a stronger level of privacy, this research data, which means that training is happening from user interaction deploys an experimental environment for FL with Differential Privacy with end devices. Federated Learning's key motivation is to provide (DP) using benchmark datasets. The obtained results show privacy protection as well as there has recently been some research that the election of parameters and techniques of DP is central in into combining the formal privacy notion of Differential Privacy the aforementioned trade-off between privacy and utility by means (DP) with FL. of a classification example.