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：本文针对线性前向模型引入了逆可行性的概念，作为增强空中计算联邦学习算法的工具。逆可行性被定义为前向算子条件数关于其参数函数的上界。我们运用此定义分析了现有空中计算联邦学习模型，识别出可改进的领域，并提出了一种新的空中计算联邦学习模型。数值实验阐明了理论结果的主要内涵。这一基于逆问题理论的框架，通过为网络提供额外的理想特性，有望对现有的安全与隐私概念形成补充。