Quantum Machine Learning for Secure Cooperative Multi-Layer Edge AI with Proportional Fairness
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915401440428032 |
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| author | Vu, Thai T. Le, John |
| author_facet | Vu, Thai T. Le, John |
| contents | This paper proposes a communication-efficient, event-triggered inference framework for cooperative edge AI systems comprising multiple user devices and edge servers. Building upon dual-threshold early-exit strategies for rare-event detection, the proposed approach extends classical single-device inference to a distributed, multi-device setting while incorporating proportional fairness constraints across users. A joint optimization framework is formulated to maximize classification utility under communication, energy, and fairness constraints. To solve the resulting problem efficiently, we exploit the monotonicity of the utility function with respect to the confidence thresholds and apply alternating optimization with Benders decomposition. Experimental results show that the proposed framework significantly enhances system-wide performance and fairness in resource allocation compared to single-device baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15145 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Machine Learning for Secure Cooperative Multi-Layer Edge AI with Proportional Fairness Vu, Thai T. Le, John Networking and Internet Architecture Machine Learning This paper proposes a communication-efficient, event-triggered inference framework for cooperative edge AI systems comprising multiple user devices and edge servers. Building upon dual-threshold early-exit strategies for rare-event detection, the proposed approach extends classical single-device inference to a distributed, multi-device setting while incorporating proportional fairness constraints across users. A joint optimization framework is formulated to maximize classification utility under communication, energy, and fairness constraints. To solve the resulting problem efficiently, we exploit the monotonicity of the utility function with respect to the confidence thresholds and apply alternating optimization with Benders decomposition. Experimental results show that the proposed framework significantly enhances system-wide performance and fairness in resource allocation compared to single-device baselines. |
| title | Quantum Machine Learning for Secure Cooperative Multi-Layer Edge AI with Proportional Fairness |
| topic | Networking and Internet Architecture Machine Learning |
| url | https://arxiv.org/abs/2507.15145 |