Modeling Quantum Federated Autoencoder for Anomaly Detection in IoT Networks

ArXi:2603.22366v1 Announce Type: cross We propose a Quantum Federated Autoencoder for Anomaly Detection, a framework that leverages quantum federated learning for efficient, secure, and distributed processing in IoT networks. By harnessing quantum autoencoders for high-dimensional feature representation and federated learning for decentralized model