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：我们引入了线性前向模型的逆可行性概念，作为增强空口联邦学习（OTA FL）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的上界。我们利用这一定义分析了现有OTA FL模型，识别出可改进之处，并提出了一个新的OTA FL模型。数值实验展示了理论结果的主要意义。这一基于逆问题理论提出的框架，通过为网络提供额外的理想特性，有望补充现有的安全与隐私概念。