Federated Learning in Cybersecurity: Enhancing Privacy and Security in Collaborative Learning Models

Abstract

Federated Learning (FL) represents a paradigm shift in machine learning, enabling collaborative model training across decentralized data sources without compromising individual privacy. This paper explores the integration of Federated Learning in cybersecurity, focusing on its potential to enhance privacy and security in collaborative learning models. We analyze the advantages and challenges associated with FL, review existing implementations in cybersecurity contexts, and propose strategies for overcoming its limitations.