When Quantum Federated Learning Meets Blockchain in 6G Networks
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866908704792641536 |
|---|---|
| author | Nguyen, Dinh C. Zami, Md Bokhtiar Al Rahman, Ratun Shaon, Shaba Nguyen, Tuy Tan Afghah, Fatemeh |
| author_facet | Nguyen, Dinh C. Zami, Md Bokhtiar Al Rahman, Ratun Shaon, Shaba Nguyen, Tuy Tan Afghah, Fatemeh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_09958 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | When Quantum Federated Learning Meets Blockchain in 6G Networks Nguyen, Dinh C. Zami, Md Bokhtiar Al Rahman, Ratun Shaon, Shaba Nguyen, Tuy Tan Afghah, Fatemeh Cryptography and Security Distributed, Parallel, and Cluster Computing 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. |
| title | When Quantum Federated Learning Meets Blockchain in 6G Networks |
| topic | Cryptography and Security Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2512.09958 |