Growing privacy concerns regarding personal data disclosure are contrasting with the constant need of such information for data-driven applications. To address this issue, the combination of federated learning and differential privacy is now well-established in the domain of machine learning. These techniques allow to train deep neural networks without collecting the data and while preventing information leakage. However, there are many scenarios where simpler and more robust machine learning models are preferable. In this paper, we present a federated and differentially-private version of the Naive Bayes algorithm for classification. Our\nresults show that, without data collection, the same performance of a centralized solution can be achieved on any dataset with only a slight increase in the privacy budget. Furthermore, if certain conditions are met, our federated solution can outperform a centralized approach.

The growing computational demands of model training tasks and the increased privacy awareness of consumers call for the development of new techniques in the area of machine learning. Fully decentralized approaches have been proposed, but are still in early research stages. This study analyses gossip learning, one of these state-of-the-art decentralized machine learning protocols, which promises high scalability and privacy preservation, with the goal of assessing its applicability to real-world scenarios.Previous research on gossip learning presents strong and often unrealistic assumptions on the distribution of the data, the communication speeds of the devices and the connectivity among them. Our results show that lifting these requirements can, in certain scenarios, lead to slow convergence of the protocol or even unfair bias in the produced models. This paper identifies the conditions in which gossip learning can and cannot be applied, and introduces extensions that mitigate some of its limitations.