Federated Learning-Based Privacy Preservation in Distributed Machine Learning Systems

Abstract

Data privacy is a major challenge in centralized machine learning systems. This paper introduces a federated learning approach that enables decentralized model training without sharing raw data. The framework incorporates secure aggregation and differential privacy techniques to protect sensitive information. Experimental analysis reveals that the proposed method achieves comparable accuracy to centralized models while significantly enhancing data privacy and reducing communication overhead.