Quantum Federated Learning: A Comprehensive Survey

TL;DR

This comprehensive survey explores quantum federated learning (QFL), combining quantum computing and federated learning to enhance privacy and efficiency in decentralized quantum systems, highlighting key concepts, applications, and future challenges.

Contribution

It provides an extensive overview of QFL's fundamentals, architectures, applications, and prototype implementations, offering insights into its current state and future research directions.

Findings

QFL integrates quantum computing with federated learning for privacy-preserving decentralized models.

Applications of QFL span vehicular, healthcare, satellite networks, and more.

Identifies key challenges and future research avenues in QFL development.

Abstract

Quantum federated learning (QFL) is a combination of distributed quantum computing and federated machine learning, integrating the strengths of both to enable privacy-preserving decentralized learning with quantum-enhanced capabilities. It appears as a promising approach for addressing challenges in efficient and secure model training across distributed quantum systems. This paper presents a comprehensive survey on QFL, exploring its key concepts, fundamentals, applications, and emerging challenges in this rapidly developing field. Specifically, we begin with an introduction to the recent advancements of QFL, followed by discussion on its market opportunity and background knowledge. We then discuss the motivation behind the integration of quantum computing and federated learning, highlighting its working principle. Moreover, we review the fundamentals of QFL and its taxonomy. Particularly, we explore federation architecture, networking topology, communication schemes, optimization techniques, and security mechanisms within QFL frameworks. Furthermore, we investigate applications of QFL across several domains which include vehicular networks, healthcare networks, satellite networks, metaverse, and network security. Additionally, we analyze frameworks and platforms related to QFL, delving into its prototype implementations, and provide a detailed case study. Key insights and lessons learned from this review of QFL are also highlighted. We complete the survey by identifying current challenges and outlining potential avenues for future research in this rapidly advancing field.