Private Collaborative Edge Inference via Over-the-Air Computation
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 pri...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Transactions on Machine Learning in Communications and Networking |
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| Online Access: | https://ieeexplore.ieee.org/document/10829586/ |
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| author | Selim F. Yilmaz Burak Hasircioglu Li Qiao Deniz Gunduz |
| author_facet | Selim F. Yilmaz Burak Hasircioglu Li Qiao Deniz Gunduz |
| author_sort | Selim F. Yilmaz |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9f5bdf818ef04549afbf7ad14814dc26 |
| institution | DOAJ |
| issn | 2831-316X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Machine Learning in Communications and Networking |
| spelling | doaj-art-9f5bdf818ef04549afbf7ad14814dc262025-08-20T03:11:06ZengIEEEIEEE Transactions on Machine Learning in Communications and Networking2831-316X2025-01-01321523110.1109/TMLCN.2025.352655110829586Private Collaborative Edge Inference via Over-the-Air ComputationSelim F. Yilmaz0https://orcid.org/0000-0002-0486-7731Burak Hasircioglu1https://orcid.org/0000-0001-5005-2894Li Qiao2https://orcid.org/0000-0003-0586-189XDeniz Gunduz3https://orcid.org/0000-0002-7725-395XDepartment of Electrical and Electronic Engineering, Imperial College London, London, U.K.The Alan Turing Institute, London, U.K.School of Information and Electronics, Beijing Institute of Technology, Beijing, ChinaDepartment of Electrical and Electronic Engineering, Imperial College London, London, U.K.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.https://ieeexplore.ieee.org/document/10829586/Edge inferencecollaborative inferencedistributed inferenceensemblemulti-viewover-the-air computation (OAC) |
| spellingShingle | Selim F. Yilmaz Burak Hasircioglu Li Qiao Deniz Gunduz Private Collaborative Edge Inference via Over-the-Air Computation IEEE Transactions on Machine Learning in Communications and Networking Edge inference collaborative inference distributed inference ensemble multi-view over-the-air computation (OAC) |
| title | Private Collaborative Edge Inference via Over-the-Air Computation |
| title_full | Private Collaborative Edge Inference via Over-the-Air Computation |
| title_fullStr | Private Collaborative Edge Inference via Over-the-Air Computation |
| title_full_unstemmed | Private Collaborative Edge Inference via Over-the-Air Computation |
| title_short | Private Collaborative Edge Inference via Over-the-Air Computation |
| title_sort | private collaborative edge inference via over the air computation |
| topic | Edge inference collaborative inference distributed inference ensemble multi-view over-the-air computation (OAC) |
| url | https://ieeexplore.ieee.org/document/10829586/ |
| work_keys_str_mv | AT selimfyilmaz privatecollaborativeedgeinferenceviaovertheaircomputation AT burakhasircioglu privatecollaborativeedgeinferenceviaovertheaircomputation AT liqiao privatecollaborativeedgeinferenceviaovertheaircomputation AT denizgunduz privatecollaborativeedgeinferenceviaovertheaircomputation |