Federated Learning: A Survey on Privacy-Preserving Collaborative Intelligence

TL;DR

This survey reviews federated learning's architecture, challenges, privacy techniques, emerging trends, and applications, emphasizing its role in privacy-preserving distributed AI across various domains.

Contribution

It provides a comprehensive overview of federated learning, covering core concepts, technical challenges, privacy mechanisms, and future research directions, which is valuable for researchers and practitioners.

Findings

Addresses key challenges like non-IID data and heterogeneity.

Discusses privacy-preserving techniques such as differential privacy.

Highlights emerging trends and real-world applications.

Abstract

Federated Learning (FL) has emerged as a transformative paradigm in the field of distributed machine learning, enabling multiple clients such as mobile devices, edge nodes, or organizations to collaboratively train a shared global model without the need to centralize sensitive data. This decentralized approach addresses growing concerns around data privacy, security, and regulatory compliance, making it particularly attractive in domains such as healthcare, finance, and smart IoT systems. This survey provides a concise yet comprehensive overview of Federated Learning, beginning with its core architecture and communication protocol. We discuss the standard FL lifecycle, including local training, model aggregation, and global updates. A particular emphasis is placed on key technical challenges such as handling non-IID (non-independent and identically distributed) data, mitigating system and hardware heterogeneity, reducing communication overhead, and ensuring privacy through mechanisms like differential privacy and secure aggregation. Furthermore, we examine emerging trends in FL research, including personalized FL, cross-device versus cross-silo settings, and integration with other paradigms such as reinforcement learning and quantum computing. We also highlight real-world applications and summarize benchmark datasets and evaluation metrics commonly used in FL research. Finally, we outline open research problems and future directions to guide the development of scalable, efficient, and trustworthy FL systems.