Federated Learning for Privacy-Preserving Machine Learning

ABSTRACTFederated Learning (FL) represents a transformative approach in the field of machine learning, offering a decentralized framework for training models without the need to exchange raw data. This project investigates the mechanisms, architectures, and privacy-preserving techniques integral to federated learning, focusing on its potential to enhance privacy and security in machine learning applications. By leveraging methods such as model aggregation, differential privacy, and secure multi-party computation, FL minimizes data exposure while maintaining robust model performance. The study critically evaluates FL architectures, including client-server and decentralized models, highlighting their trade-offs in terms of scalability, efficiency, and security. It also identifies key challenges such as data heterogeneity, communication overhead, and scalability limitations, which hinder broader adoption. Comparative analysis with traditional privacy-preserving methods underscores FL’s superiority in safeguarding sensitive information, albeit with vulnerabilities to gradient leakage and adversarial attacks. Emerging trends, including lightweight algorithms and edge computing integration, offer promising avenues for future research. This research contributes to the growing body of knowledge on privacy-preserving machine learning by providing actionable recommendations for implementation, policy formulation, and addressing research gaps. The findings hold significant implications for industries, academia, and technology, particularly in privacy-sensitive domains like healthcare and finance, where data confidentiality is paramount.Keywords: Federated Learning, Privacy Preservation, Machine Learning, Differential Privacy, Secure Multi-Party Computation, Decentralized Architectures.