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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.06078 |
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| _version_ | 1866913984191397888 |
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| author | Hsu, Yu-Chao Jiang, Jiun-Cheng Lin, Chun-Hua Chen, Wei-Ting Peng, Kuo-Chung Tiwari, Prayag Chen, Samuel Yen-Chi Kuo, En-Jui |
| author_facet | Hsu, Yu-Chao Jiang, Jiun-Cheng Lin, Chun-Hua Chen, Wei-Ting Peng, Kuo-Chung Tiwari, Prayag Chen, Samuel Yen-Chi Kuo, En-Jui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_06078 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Federated Quantum Kernel-Based Long Short-term Memory for Human Activity Recognition Hsu, Yu-Chao Jiang, Jiun-Cheng Lin, Chun-Hua Chen, Wei-Ting Peng, Kuo-Chung Tiwari, Prayag Chen, Samuel Yen-Chi Kuo, En-Jui Quantum Physics 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. |
| title | Federated Quantum Kernel-Based Long Short-term Memory for Human Activity Recognition |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2508.06078 |