We introduce the concepts of inverse solvability and security for a generic linear forward model and demonstrate how they can be applied to models used in federated learning. We provide examples of such models which differ in the resulting inverse solvability and security as defined in this paper. We also show how the large number of users participating in a given iteration of federated learning can be leveraged to increase both solvability and security. Finally, we discuss possible extensions of the presented concepts including the nonlinear case.

翻译：我们为通用线性远征模式引入逆向溶解和安全概念,并展示这些概念如何适用于联合学习中使用的模式;我们提供一些实例,说明这些模型在本文件所界定的结果相反溶解和安全性方面各不相同;我们还表明如何利用参加联合学习特定迭代的大量用户来增加溶解和安全性;最后,我们讨论提出的概念可能扩大,包括非线性案例。