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.

翻译：我们引入线性前向模型中逆可行性的概念，作为增强空中联邦学习（OTA FL）算法的工具。逆可行性定义为前向算子条件数关于其参数的上界。我们利用这一定义分析现有OTA FL模型，识别可改进之处，并提出一种新的OTA FL模型。数值实验展示了理论结果的主要含义。这一基于逆问题理论所提出的框架，能够通过为网络提供额外所需特性，对现有的安全与隐私概念形成潜在补充。