Towards Personalized Quantum Federated Learning for Anomaly Detection

2025-11-08

IEEE Transactions on Network Science and Engineering

AI Summary

To address the performance degradation of global models in label-scarce anomaly detection tasks—caused by inherent heterogeneity among clients in quantum federated networks regarding hardware capabilities, noise characteristics, and non-IID data distributions—this paper proposes the first personalized federated learning framework tailored to quantum device characteristics. Our method integrates parameterized quantum circuits with classical optimizers and introduces a quantum-centric personalization mechanism that enables adaptive local model optimization per client. Experiments conducted in realistic quantum simulation environments demonstrate that, compared to the best baseline, our approach reduces false positive rate by 23%, while improving AUROC and AUPR by 24.2% and 20.5%, respectively. These results substantiate significant gains in anomaly detection accuracy, robustness against device-specific noise and data skew, and system scalability under quantum constraints.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Anomaly detection has a significant impact on applications such as video surveillance, medical diagnostics, and industrial monitoring, where anomalies frequently depend on context and anomaly-labeled data are limited. Quantum federated learning (QFL) overcomes these concerns by distributing model training among several quantum clients, consequently eliminating the requirement for centralized quantum storage and processing. However, in real-life quantum networks, clients frequently differ in terms of hardware capabilities, circuit designs, noise levels, and how classical data is encoded or preprocessed into quantum states. These differences create inherent heterogeneity across clients - not just in their data distributions, but also in their quantum processing behaviors. As a result, training a single global model becomes ineffective, especially when clients handle imbalanced or non-identically distributed (non-IID) data. To address this, we propose a new framework called personalized quantum federated learning (PQFL) for anomaly detection. PQFL enhances local model training at quantum clients using parameterized quantum circuits and classical optimizers, while introducing a quantum-centric personalization strategy that adapts each client's model to its own hardware characteristics and data representation. Extensive experiments show that PQFL significantly improves anomaly detection accuracy under diverse and realistic conditions. Compared to state-of-the-art methods, PQFL reduces false errors by up to 23%, and achieves gains of 24.2% in AUROC and 20.5% in AUPR, highlighting its effectiveness and scalability in practical quantum federated settings.

Problem

• Addressing client heterogeneity in quantum federated learning for anomaly detection
• Overcoming hardware and data distribution differences across quantum clients
• Improving anomaly detection accuracy in diverse quantum network conditions

Innovation

• Personalized quantum federated learning for anomaly detection
• Parameterized quantum circuits with classical optimizers
• Quantum-centric personalization for hardware and data adaptation