Robust Quantum Federated Learning Against Colluding and Non-Colluding Byzantine Attacks
Summary
Quantum federated learning (a machine learning approach where multiple computers train a model together using quantum computing) can be weakened by Byzantine attacks (when some computers send bad or malicious data to sabotage the model). This paper proposes a defense method using adaptive clustering, an algorithm that groups similar data points together, to protect quantum federated learning systems from both types of Byzantine attacks happening at the same time, achieving 98% accuracy on image classification tests.
Solution / Mitigation
The source proposes an adaptive clustering-based defense algorithm extending DBSCAN (a clustering technique) and an adaptive weight allocation algorithm that serves as a server-side robust aggregation mechanism. The paper states the method achieves 98% accuracy on MNIST image classification, representing a 38-percentage-point improvement over the undefended baseline, and reduces attack-induced performance degradation in backdoor attack settings. However, no explicit software patch, version update, or deployment instructions are provided in the source text.
Classification
Related Issues
Original source: http://ieeexplore.ieee.org/document/11612922
First tracked: August 6, 2026 at 08:04 PM
Classified by LLM (prompt v3) · confidence: 85%