DAQFL: Dynamic Aggregation Quantum Federated Learning Algorithm for Intelligent Diagnosis in Internet of Medical Things

• Published in: IEEE internet of things journal Vol. 12; no. 19; pp. 39313 - 39325
• Main Authors: Qu, Zhiguo, Zhao, Xuemeng, Sun, Le, Muhammad, Ghulam
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.10.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Computational modeling; Diagnosis; Federated learning; Heterogeneity; Heuristic algorithms; Industrial applications; Intelligent diagnosis; Internet of medical things; Internet of Medical Things (IoMT); Machine learning; Machine learning algorithms; Medical diagnostic imaging; Medical services; Neural networks; Optimization; Performance degradation; Prediction algorithms; Privacy; quantum federated learning (QFL); quantum neural networks (QNNs); Training; variational quantum circuits (VQC)
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2025.3537614

Federated learning (FL) is a privacy-preserving alternative to centralized machine learning, where model training is performed on local devices and only global model updates are shared, effectively addressing challenges, such as data silos and privacy protection. Recently, quantum FL (QFL), an emerging FL branch, has garnered significant attention in many industry applications. However, existing QFL algorithms predominantly employ average weighting for global model training, which shows poor performance on heterogeneous healthcare data. To address this challenge, this study proposes a dynamic aggregation QFL algorithm (DAQFL) for intelligent diagnosis. Specifically, it utilizes quantum neural networks (QNNs) as local training models and designs corresponding variational quantum circuits (VQC). To mitigate performance degradation caused by the heterogeneity of medical industrial data, a dynamic aggregation method based on accuracy is proposed to enhance global model performance effectively. Extensive experiments with three distribution settings, including independent and identically distributed (IID), non-independent and identically distributed (Non-IID), and long-tail datasets, show that DAQFL outperforms baseline algorithms in accuracy and training speed. It also performs well in privacy protection and robustness of anti-noise, improving its suitability for real-world medical applications.