When Quantum Federated Learning Meets Blockchain in 6G Networks

TL;DR

This paper introduces QFLchain, a framework combining quantum federated learning with blockchain technology to enhance security, scalability, and efficiency for 6G network intelligence, addressing dynamic and decentralized environments.

Contribution

The paper proposes a novel integration of quantum federated learning and blockchain, providing a scalable, secure, and efficient framework for 6G networks, with detailed analysis and a case study.

Findings

QFLchain improves training performance over existing methods.

It effectively addresses security vulnerabilities in 6G environments.

The framework reduces communication, storage, and energy overheads.

Abstract

Quantum federated learning (QFL) is emerging as a key enabler for intelligent, secure, and privacy-preserving model training in next-generation 6G networks. By leveraging the computational advantages of quantum devices, QFL offers significant improvements in learning efficiency and resilience against quantum-era threats. However, future 6G environments are expected to be highly dynamic, decentralized, and data-intensive, which necessitates moving beyond traditional centralized federated learning frameworks. To meet this demand, blockchain technology provides a decentralized, tamper-resistant infrastructure capable of enabling trustless collaboration among distributed quantum edge devices. This paper presents QFLchain, a novel framework that integrates QFL with blockchain to support scalable and secure 6G intelligence. In this work, we investigate four key pillars of \textit{QFLchain} in the 6G context: (i) communication and consensus overhead, (ii) scalability and storage overhead, (iii) energy inefficiency, and (iv) security vulnerability. A case study is also presented, demonstrating potential advantages of QFLchain, based on simulation, over state-of-the-art approaches in terms of training performance.