Conventional and Quantum Feature Selection and Federated Learning Applications for Anomaly Detection in IoT Healthcare Networks

Privacy is a major concern in the Internet Healthcare of Things (IoHT), where threat actors may intrude systems to access personally identifiable data. Federated Learning (FL) is a well suited Machine Learning (ML) approach to preserve confidentiality, availability, and integrity in such settings during data analysis. In this work, we introduce a Network of Quantum ML (N-QML) approach for IoHT intrusion detection, integrating quantum PCA (QPCA) with Quantum FL (QPCA+QFL), tested on a subset of the data set WUSTL-EHMS-2020 using classical and quantum computing experiments across three seeds, compared against conventional ML (CML) on three feature dimensions. Among CML techniques, the integration of PCA and ANN (PCA+ANN) attained the best mean accuracy, 76.6%, using two features. Among QML techniques, QPCA+QNN achieved the best centralized accuracy, 74.4%, when two features are used, while PCA+SVM outperformed QPCA+QSVM using ten features (70.1% versus 66.9%). As a result of the study, during federation of the quantum model, accuracy was realized to be reduced steadily from 62.9% to 54.7% as dimensionality changed and highest variance attainment occurred at the smallest dimension; this is a pattern that was not previously documented under matched, multi-seed validation. We attribute this to FedAvg interacting with the loss landscape of quantum-derived features on small client partitions, contributing this finding and the framework as groundwork for quantum-aware federated aggregation.