  We consider collaborative inference at the wireless edge, where each client's
model is trained independently on their local datasets. 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.
Specifically, we propose different methods for ensemble and multi-view
classification that exploit over-the-air computation. 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 over-the-air multi-user inference approach 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.
