FL-QDSNNs: Federated Learning with Quantum Dynamic Spiking Neural Networks
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916924067151872 |
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| author | Innan, Nouhaila Marchisio, Alberto Shafique, Muhammad |
| author_facet | Innan, Nouhaila Marchisio, Alberto Shafique, Muhammad |
| contents | We present Federated Learning-Quantum Dynamic Spiking Neural Networks (FL-QDSNNs), a privacy-preserving framework that maintains high predictive accuracy on non-IID client data. Its key innovation is a dynamic-threshold spiking mechanism that triggers quantum gates only when local data drift requires added expressiveness, limiting circuit depth and countering the accuracy loss typical of heterogeneous clients. Evaluated on different benchmark datasets, including Iris, where FL-QDSNNs reach 94% accuracy, the approach consistently surpasses state-of-the-art quantum-federated baselines; scaling analyses demonstrate that performance remains high as the federation expands to 25 clients, confirming both computational efficiency and collaboration robustness. By uniting adaptive quantum expressiveness with strict data locality, FL-QDSNNs enable regulation-compliant quantum learning for privacy-sensitive sectors and critical infrastructure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02293 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FL-QDSNNs: Federated Learning with Quantum Dynamic Spiking Neural Networks Innan, Nouhaila Marchisio, Alberto Shafique, Muhammad Quantum Physics Emerging Technologies We present Federated Learning-Quantum Dynamic Spiking Neural Networks (FL-QDSNNs), a privacy-preserving framework that maintains high predictive accuracy on non-IID client data. Its key innovation is a dynamic-threshold spiking mechanism that triggers quantum gates only when local data drift requires added expressiveness, limiting circuit depth and countering the accuracy loss typical of heterogeneous clients. Evaluated on different benchmark datasets, including Iris, where FL-QDSNNs reach 94% accuracy, the approach consistently surpasses state-of-the-art quantum-federated baselines; scaling analyses demonstrate that performance remains high as the federation expands to 25 clients, confirming both computational efficiency and collaboration robustness. By uniting adaptive quantum expressiveness with strict data locality, FL-QDSNNs enable regulation-compliant quantum learning for privacy-sensitive sectors and critical infrastructure. |
| title | FL-QDSNNs: Federated Learning with Quantum Dynamic Spiking Neural Networks |
| topic | Quantum Physics Emerging Technologies |
| url | https://arxiv.org/abs/2412.02293 |