Inverse Feasibility in Over-the-Air Federated Learning
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Abstract (arXiv)
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.
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6 of 20 references.
References · 6
- 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
- 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 459
- 2019Federated Learning over Wireless Fading Channelscited by 183
- 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 129
- 2019On Safeguarding Privacy and Security in the Framework of Federated Learningcited by 55
- 2020A Secure Federated Learning Framework for 5G Networkscited by 32
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