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

TL;DR

This paper introduces a communication-efficient, fair, multi-device inference framework for cooperative edge AI, improving resource utilization and fairness through joint optimization and early-exit strategies.

Contribution

It extends single-device early-exit inference to a multi-device setting with proportional fairness, formulating and solving a joint optimization problem for resource allocation.

Findings

Significant performance improvements over single-device baselines.

Enhanced fairness in resource allocation among users.

Efficient optimization via monotonic utility and Benders decomposition.

Abstract

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.