Federated Learning for Privacy-Preserving AI: A Decentralized Approach to Model Training

Abstract

Traditional AI models rely on centralized data storage, raising privacy concerns and regulatory challenges. Federated learning (FL) offers a decentralized approach by enabling model training across multiple devices without sharing raw data. This paper explores the potential of FL in privacy-sensitive domains such as healthcare, finance, and edge computing. We propose an optimized FL framework incorporating differential privacy and secure aggregation techniques to enhance security while maintaining model accuracy. The study evaluates the framework on real-world datasets, demonstrating its effectiveness in achieving privacy-preserving AI solutions without compromising performance.