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：我们针对一般线性正向模型引入了逆可解性与安全性的概念，并展示了如何将其应用于联邦学习中的模型。本文提供了多个此类模型的示例，这些模型在所定义的逆可解性与安全性上存在差异。我们还展示了如何利用参与联邦学习特定迭代的大量用户来同时提升可解性和安全性。最后，讨论了所提出概念的可能扩展，包括非线性情形。