A Hybrid Quantum Neural Network for Split Learning
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| Yayımlandı: | arXiv.org (Sep 25, 2024), p. n/a |
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| Yazar: | |
| Diğer Yazarlar: | , , |
| Baskı/Yayın Bilgisi: |
Cornell University Library, arXiv.org
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| Online Erişim: | Citation/Abstract Full text outside of ProQuest |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3110125671 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3110125671 | ||
| 045 | 0 | |b d20240925 | |
| 100 | 1 | |a Cowlessur, Hevish | |
| 245 | 1 | |a A Hybrid Quantum Neural Network for Split Learning | |
| 260 | |b Cornell University Library, arXiv.org |c Sep 25, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Quantum Machine Learning (QML) is an emerging field of research with potential applications to distributed collaborative learning, such as Split Learning (SL). SL allows resource-constrained clients to collaboratively train ML models with a server, reduce their computational overhead, and enable data privacy by avoiding raw data sharing. Although QML with SL has been studied, the problem remains open in resource-constrained environments where clients lack quantum computing capabilities. Additionally, data privacy leakage between client and server in SL poses risks of reconstruction attacks on the server side. To address these issues, we propose Hybrid Quantum Split Learning (HQSL), an application of Hybrid QML in SL. HQSL enables classical clients to train models with a hybrid quantum server and curtails reconstruction attacks. In addition, we introduce a novel qubit-efficient data-loading technique for designing a quantum layer in HQSL, minimizing both the number of qubits and circuit depth. Experiments on five datasets demonstrate HQSL's feasibility and ability to enhance classification performance compared to its classical models. Notably, HQSL achieves mean improvements of over 3% in both accuracy and F1-score for the Fashion-MNIST dataset, and over 1.5% in both metrics for the Speech Commands dataset. We expand these studies to include up to 100 clients, confirming HQSL's scalability. Moreover, we introduce a noise-based defense mechanism to tackle reconstruction attacks on the server side. Overall, HQSL enables classical clients to collaboratively train their models with a hybrid quantum server, leveraging quantum advantages while improving model performance and security against data privacy leakage-related reconstruction attacks. | |
| 653 | |a Datasets | ||
| 653 | |a Quantum computing | ||
| 653 | |a Clients | ||
| 653 | |a Neural networks | ||
| 653 | |a Servers | ||
| 653 | |a Privacy | ||
| 653 | |a Reconstruction | ||
| 653 | |a Machine learning | ||
| 653 | |a Constraints | ||
| 653 | |a Leakage | ||
| 653 | |a Qubits (quantum computing) | ||
| 700 | 1 | |a Thapa, Chandra | |
| 700 | 1 | |a Alpcan, Tansu | |
| 700 | 1 | |a Camtepe, Seyit | |
| 773 | 0 | |t arXiv.org |g (Sep 25, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3110125671/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2409.16593 |