# A Simulation Study on the Theoretical Potential of Quantum-Enhanced Federated Security Operations

## TL;DR

This paper highlights a major vulnerability in a common federated learning algorithm and shows that quantum-enhanced security is not yet feasible due to significant technical and financial barriers.

## Contribution

Identifies a critical vulnerability in Krum and evaluates quantum-enhanced security feasibility through simulation.

## Key findings

- Krum fails catastrophically in high-dimensional neural networks, degrading accuracy by 44.7%.
- Quantum-enhanced security faces barriers like high EMI, costs of USD 3–5M, and scalability limits to 20 nodes.
- FLTrust is more resilient than Krum but requires trusted infrastructure.

## Abstract

This paper makes two distinct contributions to the security and federated learning communities. First, we identify and empirically demonstrate a critical vulnerability in Krum, a widely deployed Byzantine-resilient aggregation algorithm, showing catastrophic failure (44.7% accuracy degradation) when applied to high-dimensional neural networks. We provide comprehensive analysis of five alternative algorithms and validate FLTrust as a more resilient solution, though requiring trusted infrastructure. This finding has immediate implications for production federated learning systems. Second, we present a rigorous feasibility analysis of quantum-enhanced security operations through simulation-based exploration. We document fundamental deployment barriers including (1) environmental electromagnetic interference exceeding sensor capabilities by 6-9 orders of magnitude, (2) infrastructure costs of USD 3–5M with unproven benefits, (3) an absence of validated correlation mechanisms between quantum measurements and cyber threats, and (4) O(n2) scalability constraints limiting deployments to 20 nodes. This is purely theoretical research using simulated data without physical quantum sensors. Physical validation through empirical noise characterization and sensor deployment in operational environments represents the critical next step, though faces significant challenges from EMI shielding requirements and calibration procedures. Together, these contributions provide actionable insights for current federated learning deployments while preventing premature investment in quantum sensing for cybersecurity.