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

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

来源:Arxiv_logoArxiv
英文摘要

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.

Yu-Chao Hsu、Jiun-Cheng Jiang、Chun-Hua Lin、Wei-Ting Chen、Kuo-Chung Peng、Prayag Tiwari、Samuel Yen-Chi Chen、En-Jui Kuo

计算技术、计算机技术

Yu-Chao Hsu,Jiun-Cheng Jiang,Chun-Hua Lin,Wei-Ting Chen,Kuo-Chung Peng,Prayag Tiwari,Samuel Yen-Chi Chen,En-Jui Kuo.Federated Quantum Kernel-Based Long Short-term Memory for Human Activity Recognition[EB/OL].(2025-08-11)[2025-08-24].https://arxiv.org/abs/2508.06078.点此复制

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