Federated Learning for Privacy-Preserving AI: A Comparative Analysis of Decentralized Data Training

Abstract

The rapid adoption of Artificial Intelligence (AI) across industries, particularly in healthcare, finance, and smart devices, has introduced significant concerns regarding data privacy, security, and compliance with regulations such as GDPR, HIPAA, and CCPA. Traditional centralized machine learning (ML) models require large-scale data aggregation, increasing risks of data breaches, misuse, and unauthorized access. Federated Learning (FL) has emerged as a transformative solution, allowing multiple edge devices or organizations to collaboratively train machine learning models without sharing raw data. This paper explores the principles, advantages, and challenges of FL and conducts an empirical analysis comparing FL’s efficacy, security, and scalability to centralized models. A case study on federated learning in healthcare diagnostics highlights the real-world impact of this approach. Additionally, insights from a structured survey of AI researchers, data scientists, and industry professionals are analyzed to assess FL adoption, technical challenges, and future potential. Findings suggest that FL enhances privacy and compliance, making it particularly suitable for industries handling sensitive information. However, challenges such as high computational costs, model convergence issues, and communication overhead must be addressed for FL to achieve widespread adoption. Future advancements in efficient federated learning frameworks, regulatory standardization, and privacy-preserving AI techniques will further define FL’s role in the evolution of decentralized artificial intelligence.