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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2211.14115 |
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| _version_ | 1866914808406736896 |
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| author | Piotrowski, Tomasz Ismayilov, Rafail Frey, Matthias Cavalcante, Renato L. G. |
| author_facet | Piotrowski, Tomasz Ismayilov, Rafail Frey, Matthias Cavalcante, Renato L. G. |
| contents | We introduce the concept of inverse feasibility for linear forward models as a tool to enhance OTA FL algorithms. Inverse feasibility is defined as an upper bound on the condition number of the forward operator as a function of its parameters. We analyze an existing OTA FL model using this definition, identify areas for improvement, and propose a new OTA FL model. Numerical experiments illustrate the main implications of the theoretical results. The proposed framework, which is based on inverse problem theory, can potentially complement existing notions of security and privacy by providing additional desirable characteristics to networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_14115 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Inverse Feasibility in Over-the-Air Federated Learning Piotrowski, Tomasz Ismayilov, Rafail Frey, Matthias Cavalcante, Renato L. G. Machine Learning We introduce the concept of inverse feasibility for linear forward models as a tool to enhance OTA FL algorithms. Inverse feasibility is defined as an upper bound on the condition number of the forward operator as a function of its parameters. We analyze an existing OTA FL model using this definition, identify areas for improvement, and propose a new OTA FL model. Numerical experiments illustrate the main implications of the theoretical results. The proposed framework, which is based on inverse problem theory, can potentially complement existing notions of security and privacy by providing additional desirable characteristics to networks. |
| title | Inverse Feasibility in Over-the-Air Federated Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2211.14115 |