Enhancing data privacy in federated learning using artificial intelligence

Federated Learning (FL) is a decentralised approach to machine learning that enables model training on local devices without the need to share raw data. While FL inherently provides some level of privacy, significant challenges remain in fully protecting sensitive information. This article explores advanced artificial intelligence (AI) techniques for improving privacy in federated learning. I propose novel methods that include differential privacy, homomorphic encryption, and secure multiparty computation, enhanced by AI-driven optimizations. My empirical studies and theoretical analyses demonstrate the effectiveness and efficiency of these techniques in maintaining data protection.