CRLGApr 26, 2023

Secure Communication Model For Quantum Federated Learning: A Post Quantum Cryptography (PQC) Framework

arXiv:2304.13413v15 citationsh-index: 26
Originality Synthesis-oriented
AI Analysis

This addresses security vulnerabilities in quantum federated learning for researchers and practitioners, but it appears incremental as it builds on existing concepts.

The paper tackles the problem of securing quantum federated learning against quantum computing threats by designing a post-quantum cryptography framework, resulting in a model with dynamic server selection and analyzed convergence and security conditions.

We design a model of Post Quantum Cryptography (PQC) Quantum Federated Learning (QFL). We develop a framework with a dynamic server selection and study convergence and security conditions. The implementation and results are publicly available1.

Code Implementations1 repo
Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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