论文标题
自适应差距纠缠多项式编码用于边缘多方计算的多项式编码
Adaptive Gap Entangled Polynomial Coding for Multi-Party Computation at the Edge
论文作者
论文摘要
多方计算（MPC）有望在边缘网络设计隐私机器学习算法。新兴方法是编码MPC（CMPC），该方法主张使用编码计算来改善MPC的性能，以计算所需的工人数量。设计CMPC算法的当前方法是将有效的编码计算结构与MPC相结合。相反，我们提出了新的建筑。自适应间隙纠缠多项式（年龄）代码，其中在MPC中优化了计算中使用的多项式程度。我们表明，根据所需的工人数量以及存储，通信和计算负载，具有年龄代码（AGE-CMPC）的MPC比现有CMPC算法的表现更好。
Multi-party computation (MPC) is promising for designing privacy-preserving machine learning algorithms at edge networks. An emerging approach is coded-MPC (CMPC), which advocates the use of coded computation to improve the performance of MPC in terms of the required number of workers involved in computations. The current approach for designing CMPC algorithms is to merely combine efficient coded computation constructions with MPC. Instead, we propose a new construction; Adaptive Gap Entangled polynomial (AGE) codes, where the degrees of polynomials used in computations are optimized for MPC. We show that MPC with AGE codes (AGE-CMPC) performs better than existing CMPC algorithms in terms of the required number of workers as well as storage, communication and computation load.
