论文标题
关于（安全）多方计算与（安全）联合学习之间的关系
On the relationship between (secure) multi-party computation and (secure) federated learning
论文作者
论文摘要
此简短说明的贡献包含以下两个部分：在第一部分中，我们能够证明Kairouz等人提出的联邦学习（FL）程序。 \ cite {kairouz1901}，是一个随机处理。也就是说，可以在多方计算（MPC）的背景下定义FL过程的$ M $ ARY功能；此外，沿Kairouz等人的定义沿FL协议的实例可以看作是定义的$ M $ - ARY功能的实现。因此，FL过程的实例也是MPC协议的实例。简而言之，FL是MPC的子集。 要私下计算定义的FL（M-ARY）功能，已经部署了各种技术，例如同构加密（HE），安全的多方计算（SMPC）和差异隐私（DP）。在第二部分中，我们能够证明，如果基础实例私下计算基于仿真的框架中定义的$ m $ - ary功能，则基于仿真的FL解决方案也是SMPC的实例。因此，SFL是SMPC的子集。
The contribution of this short note, contains the following two parts: in the first part, we are able to show that the federate learning (FL) procedure presented by Kairouz et al. \cite{Kairouz1901}, is a random processing. Namely, an $m$-ary functionality for the FL procedure can be defined in the context of multi-party computation (MPC); Furthermore, an instance of FL protocol along Kairouz et al.'s definition can be viewed as an implementation of the defined $m$-ary functionality. As such, an instance of FL procedure is also an instance of MPC protocol. In short, FL is a subset of MPC. To privately computing the defined FL (m-ary) functionality, various techniques such as homomorphic encryption (HE), secure multi-party computation (SMPC) and differential privacy (DP) have been deployed. In the second part, we are able to show that if the underlying FL instance privately computes the defined $m$-ary functionality in the simulation-based framework, then the simulation-based FL solution is also an instance of SMPC. Consequently, SFL is a subset of SMPC.
