Federated quantum-inspired anomaly detection using collaborative neural clients

Abstract

The fusion of deep-learning-based and federated methods has brought great progress in anomaly detection. Yet the systems of today still suffer from certain glaring issues. First, aggregation of data on a central entity poses dangerous privacy hazards. Second, such models could not scale and adapt to heterogeneous and distributed environments. Lastly, fine consideration has hardly been given to quantum-inspired computational paradigms that may promise to improve both speed and security of such systems. To fill in these gaps, this research proposes a completely novel quantum-inspired federated learning approach to anomaly detection that keeps data private and allows for further implementations of quantum computing applications.