Private Collaborative Edge Inference via Over-the-Air Computation

TL;DR

This paper introduces privacy-preserving, bandwidth-efficient collaborative inference methods at the wireless edge using over-the-air computation, outperforming traditional orthogonal approaches in accuracy and resource usage.

Contribution

It proposes novel over-the-air computation schemes for multi-user inference that enhance privacy and efficiency, validated through experiments and ablation studies.

Findings

OAC schemes outperform orthogonal methods statistically.

Proposed methods use fewer resources while maintaining accuracy.

Experimental results confirm the effectiveness of the approach.

Abstract

We consider collaborative inference at the wireless edge, where each client's model is trained independently on its local dataset. Clients are queried in parallel to make an accurate decision collaboratively. In addition to maximizing the inference accuracy, we also want to ensure the privacy of local models. To this end, we leverage the superposition property of the multiple access channel to implement bandwidth-efficient multi-user inference methods. We propose different methods for ensemble and multi-view classification that exploit over-the-air computation (OAC). We show that these schemes perform better than their orthogonal counterparts with statistically significant differences while using fewer resources and providing privacy guarantees. We also provide experimental results verifying the benefits of the proposed OAC approach to multi-user inference, and perform an ablation study to demonstrate the effectiveness of our design choices. We share the source code of the framework publicly on Github to facilitate further research and reproducibility.