# Quantum Machine Learning for Secure Cooperative Multi-Layer Edge AI with Proportional Fairness

arXiv: https://arxiv.org/abs/2507.15145 · HTML: https://mlsanity.com/paper/2507.15145/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 20 Jul 2025.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks](https://mlsanity.com/paper/1709.01686/) (2017)
- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (2020)
- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
- [Deep Reinforcement Learning Based Intelligent Reflecting Surface for Secure Wireless Communications](https://mlsanity.com/paper/2002.12271/) (2020)
- [Resource Allocation for Multiuser Edge Inference with Batching and Early Exiting (Extended Version)](https://mlsanity.com/paper/2204.05223/) (2022)

## Abstract (arXiv)

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.