arXiv:2605.27416v1 Announce Type: cross Abstract: Quantum Federated Learning (QFL) inherits the core vulnerability of federated optimization to malicious clients, while also introducing an attack surface from variational circuit training and measurement-driven gradients. This work proposes a novel CircUit-Level backdoor Threat (CULT) model that formalizes four stealthy attacks by exploiting quantum-aware mechanisms, including Grover, Pauli, Bit-flip, and Sign-flip. By enabling malicious clients on both in-training and post-training surfaces, these attacks can critically undermine the learning
The proliferation of quantum computing research and the nascent development of Quantum Federated Learning frameworks makes this a timely exploration of inherent security vulnerabilities before widespread adoption.
Understanding and addressing fundamental security vulnerabilities in Quantum Federated Learning is critical for future trust and deployment of quantum AI applications, especially in sensitive domains.
The focus in quantum AI security shifts to include circuit-level backdoor threats, demanding specific defenses beyond traditional federated learning security models.
- · Quantum security researchers
- · Organizations developing secure QFL platforms
- · Ethical AI developers
- · Malicious actors exploiting quantum vulnerabilities
- · QFL users without robust security measures
- · Early, insecure QFL implementations
Increased research and development into quantum-resistant security protocols for federated learning.
Potential for a 'quantum security arms race' as new vulnerabilities are discovered and patched.
Delayed broad adoption of Quantum Federated Learning until robust security frameworks are established, influencing investment and deployment timelines.
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