Inverse Feasibility in Over-the-Air Federated Learning
arXiv (Cornell University)(2022)
摘要
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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