Over-the-air ensemble inference with model privacy
File(s)
Accepted version
Author(s)
Yilmaz, Selim F
Hasircioglu, Burak
Gunduz, Deniz
Type
Conference Paper
Abstract
We consider distributed inference at the wireless edge, where multiple clients with an ensemble of models, each trained independently on a local dataset, are queried in parallel to make an accurate decision on a new sample. In addition to maximizing inference accuracy, we also want to maximize the privacy of local models. We exploit the superposition property of the air to implement bandwidth-efficient ensemble inference methods. We introduce different over-the-air ensemble methods and show that these schemes perform significantly better than their orthogonal counterparts, while using less resources and providing privacy guarantees. We also provide experimental results verifying the benefits of the proposed over-the-air inference approach, whose source code is shared publicly on Github.
Date Issued
2022-08-03
Date Acceptance
2022-08-01
Citation
2022 IEEE International Symposium on Information Theory (ISIT), 2022, pp.1265-1270
Publisher
IEEE
Start Page
1265
End Page
1270
Journal / Book Title
2022 IEEE International Symposium on Information Theory (ISIT)
Identifier
https://ieeexplore.ieee.org/document/9834591
Source
2022 IEEE International Symposium on Information Theory (ISIT)
Publication Status
Published
Start Date
2022-06-26
Finish Date
2022-07-01
Coverage Spatial
Espoo, Finland
Date Publish Online
2022-08-03