Private collaborative edge inference via over-the-air computation
File(s)
Author(s)
Yilmaz, Selim F
Hasircioğlu, Burak
Qiao, Li
Gündüz, Denız
Type
Journal Article
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.
Date Issued
2025-01-01
Date Acceptance
2024-12-31
Citation
IEEE Transactions on Machine Learning in Communications and Networking, 2025, 3, pp.215-231
ISSN
2831-316X
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Start Page
215
End Page
231
Journal / Book Title
IEEE Transactions on Machine Learning in Communications and Networking
Volume
3
Copyright Statement
2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
License URL
Identifier
10.1109/TMLCN.2025.3526551
Subjects
Edge inference
collaborative inference
distributed inference
ensemble
multi-view
overthe-air computation (OAC)
wireless communications
differential privacy
multi-class classification
privacyutility trade-off
Publication Status
Published
Date Publish Online
2025-01-06