EdgeCloud-Reasoning

Mechanism-Designed Belief Aggregation for Edge Intelligence

EdgeCloud-Reasoning applies mechanism design theory to the problem of aggregating beliefs from distributed edge AI agents, ensuring truthful reporting through incentive-compatible protocols. In edge computing scenarios where multiple lightweight models must collaborate on decisions, traditional averaging or voting can be gamed or produce suboptimal results when agents have heterogeneous capabilities.

The system implements a VCG (Vickrey-Clarke-Groves) mechanism adapted for continuous belief spaces, where each edge agent reports its belief distribution and the mechanism computes payments that make truthful reporting a dominant strategy. The VCG-based aggregation achieves a false negative rate of just 0.027, significantly better than neural aggregation baselines at 0.045.

A key engineering contribution is the computational efficiency: the mechanism achieves 4x data efficiency compared to neural alternatives (requiring far fewer calibration samples) and runs in sub-2ms at 200 concurrent agents. This is achieved through a closed-form payment computation for Gaussian belief families and efficient batch processing using DeepSets and Transformer architectures for the scoring function.

The framework generalizes to arbitrary belief aggregation tasks beyond classification, including regression, ranking, and structured prediction at the edge, making it a principled foundation for trustworthy distributed AI decision-making.

• VCG mechanism false negative rate 0.027 vs neural aggregation baseline 0.045
• 4x data-efficient compared to neural alternatives for calibration
• Sub-2ms latency at 200 concurrent edge agents
• Incentive-compatible: truthful reporting is a dominant strategy for all agents
• Generalizes to regression, ranking, and structured prediction at the edge