Federated Quantum Machine Learning with Privacy-Preserving Measurement Statistics for Edge Analytics

Edge analytics requires continuous model adaptation under tight latency, bandwidth, and confidentiality constraints, yet conventional federated learning can leak sensitive information through shared updates and often struggles under non-IID client data. This study introduces Federated Quantum Machine Learning that transmits privacy-preserving measurement statistics derived from hybrid parameterized quantum circuits rather than raw samples or high-dimensional gradients. Across 50 edge clients over 20 rounds, the proposed method achieves 0.844 accuracy and 0.836 macro-F1 with ECE 0.048, outperforming a classical FedAvg baseline (0.801 accuracy, 0.788 macro-F1, ECE 0.062) while maintaining predictable edge overhead (845 ms average round latency) and compact messaging (3.1 KB average payload per round). Privacy–measurement sweeps demonstrate accuracy saturation with increasing privacy budget, improving from 0.816 at ε = 0.5 to 0.856 at ε = 8.0 under 512 shots, while calibration improves more sharply, with ECE decreasing from 0.068 to 0.046 over the same range. Increasing shot budget from 128 to 2048 consistently increases accuracy under all privacy settings, confirming that measurement variance is a controllable bottleneck for protected learning. Communication analysis shows that statistics-only messaging remains an order of magnitude smaller than gradient-style baselines (33.9 KB average payload per round), and secure aggregation adds bounded overhead (4.8 KB average payload) with a modest utility shift (0.836 accuracy, ECE 0.051). Robustness experiments under severe non-IID drift (severity 0.9) preserve macro-F1 at 0.801 with expectation-based statistics versus 0.772 for histogram-based statistics, and perturbed-client tests show improved stability with robust aggregation, sustaining 0.836 accuracy at 10% perturbed participation compared to 0.824 under naïve aggregation. These results indicate that low-dimensional, privatized measurement summaries enable practical, reliable, and privacy-aligned federated quantum learning on constrained edge infrastructures.