Federated Learning for Privacy-Preserving AI: A Decentralized Approach to Secure Data Processing

Abstract

With growing concerns over data privacy and security, federated learning (FL) has emerged as a promising approach to training AI models without centralizing sensitive data. This paper explores the effectiveness of FL in preserving user privacy while maintaining high model accuracy. Various optimization techniques, such as differential privacy and secure aggregation, are analyzed to enhance FL’s robustness. Experimental results on medical and financial datasets demonstrate the feasibility of FL for privacy-preserving AI, highlighting its potential in industries requiring strict data protection.