Federated Learning with Differential Privacy: Enhancing Data Security in AI

Abstract

Federated learning presents a paradigm shift in AI, allowing multiple parties to collaboratively train models without sharing their data directly. However, concerns over data privacy persist, particularly in scenarios where sensitive information is involved. This paper explores the integration of differential privacy techniques into federated learning frameworks as a means to enhance data security. By injecting noise into the gradients exchanged during model training, differential privacy offers a rigorous mathematical framework to quantify and control the privacy guarantees provided to individual data contributors. This approach not only safeguards against potential data breaches and unauthorized access but also enables compliant handling of personal data under stringent privacy regulations. Through a comprehensive review of existing methodologies and experimental evaluations, this study demonstrates the feasibility and efficacy of federated learning with differential privacy in diverse application domains, including healthcare, finance, and telecommunications. The findings underscore the critical role of differential privacy in fostering trust among stakeholders and promoting the responsible deployment of AI technologies in sensitive environments.