Robust Quantum Federated Learning Against Colluding and Non-Colluding Byzantine Attacks

Summary

This paper proposes a robust quantum federated learning method that defends against both colluding and non-colluding Byzantine attacks. It extends DBSCAN into an adaptive clustering-based defense with adaptive weight allocation, serving as a server-side robust aggregation mechanism. In experiments on MNIST and Fashion-MNIST, including label-flipping and backdoor attacks, MNIST accuracy reached 98%, a 38-percentage-point gain over the undefended baseline.

Mitigation

The proposed adaptive clustering-based defense algorithm, a server-side robust aggregation mechanism for quantum federated learning under mixed Byzantine attack settings.

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