Federated Learning Techniques for Privacy-Preserving AI in Distributed Networks: A Review

Abstract

Federated Learning (FL) represents a transformative paradigm in artificial intelligence (AI), designed to enable decentralized model training across distributed data sources without requiring the direct exchange of raw data. Unlike traditional centralized learning approaches that aggregate datasets in a single location, often raising privacy, security, and compliance concerns, FL allows multiple clients, such as mobile devices, edge nodes, or institutional servers, to collaboratively train a shared global model while preserving local data confidentiality. This decentralized approach has made FL a key enabler of privacy-preserving AI, particularly in sensitive domains such as healthcare, finance, and industrial automation. This paper provides a comprehensive exploration of privacy-preserving mechanisms within federated learning, focusing on techniques such as Differential Privacy (DP), Homomorphic Encryption (HE), Secure Multi-Party Computation (SMPC), and blockchain-based frameworks. It further examines communication efficiency and system optimization strategies, including model compression, adaptive aggregation, and energy-efficient computation for resource-constrained devices. The study also highlights real-world applications of FL in Internet of Things (IoT), smart cities, and biomedical data analysis, showcasing its versatility across diverse distributed environments. Additionally, key challenges, such as data heterogeneity, scalability, and security vulnerabilities, are analyzed alongside a comparative assessment of leading FL algorithms and privacy techniques. Ultimately, this paper underscores how federated learning bridges the gap between high-performance AI and stringent data privacy requirements, presenting future research directions for robust, transparent, and privacy-enhanced intelligent systems.