Enhancing Cybersecurity through Federated Learning: A Privacy-Preserving Approach

Abstract

With the increasing concern over data privacy, federated learning has emerged as a promising paradigm for training machine learning models across decentralized devices. This paper proposes a novel framework that integrates federated learning with advanced encryption techniques to enhance cybersecurity while preserving user privacy. We present experimental results demonstrating the effectiveness of our approach in protecting sensitive data and mitigating potential security threats.