Federated Learning: Enhancing Data Privacy and Security in Machine Learning through Decentralized Training Paradigms

Abstract

Federated learning represents a transformative paradigm in the realm of machine learning by enabling models to be trained across multiple decentralized devices or servers holding local data samples without exchanging them. This approach significantly enhances data privacy and security by ensuring that sensitive information remains localized while still contributing to global model improvements. This paper delves into the technical intricacies of federated learning, examining its architecture, methodologies, and algorithms. Furthermore, it explores various applications across different industries, evaluates the challenges in implementation, and discusses potential solutions to overcome these obstacles. The impact of federated learning on data privacy and security is thoroughly analyzed, providing insights into its future scope and areas for further research.