Federated Quantum Kernel-Based Long Short-term Memory for Human Activity Recognition

• URL: http://arxiv.org/abs/2508.06078v2
• Date: Mon, 11 Aug 2025 06:25:26 GMT
• Title: Federated Quantum Kernel-Based Long Short-term Memory for Human Activity Recognition
• Authors: Yu-Chao Hsu, Jiun-Cheng Jiang, Chun-Hua Lin, Wei-Ting Chen, Kuo-Chung Peng, Prayag Tiwari, Samuel Yen-Chi Chen, En-Jui Kuo,
• Abstract summary: We introduce the Federated Quantum Kernel-Based Long Short-term Memory (Fed-QK-LSTM) framework.<n>Within Fed-QK-LSTM framework, we enhance human activity recognition in privacy-sensitive environments.<n>We showcase the potential of Fed-QK-LSTM framework for robust and privacy-preserving human activity recognition in real-world applications.
• Score: 12.82920414864798
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this work, we introduce the Federated Quantum Kernel-Based Long Short-term Memory (Fed-QK-LSTM) framework, integrating the quantum kernel methods and Long Short-term Memory into federated learning. Within Fed-QK-LSTM framework, we enhance human activity recognition (HAR) in privacy-sensitive environments and leverage quantum computing for distributed learning systems. The DeepConv-QK-LSTM architecture on each client node employs convolutional layers for efficient local pattern capture, this design enables the use of a shallow QK-LSTM to model long-range relationships within the HAR data. The quantum kernel method enables the model to capture complex non-linear relationships in multivariate time-series data with fewer trainable parameters. Experimental results on RealWorld HAR dataset demonstrate that Fed-QK-LSTM framework achieves competitive accuracy across different client settings and local training rounds. We showcase the potential of Fed-QK-LSTM framework for robust and privacy-preserving human activity recognition in real-world applications, especially in edge computing environments and on scarce quantum devices.

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