Multi-party computation (MPC) is promising for privacy-preserving machine learning algorithms at edge networks, like federated learning. Despite their potential, existing MPC algorithms fail short of adapting to the limited resources of edge devices. A promising solution, and the focus of this work, is coded computation, which advocates the use of error-correcting codes to improve the performance of distributed computing through ``smart'' data redundancy. In this paper, we focus on coded privacy-preserving computation using Shamir's secret sharing. In particular, we design novel coded privacy-preserving computation mechanisms; MatDot coded MPC (MatDot-CMPC) and PolyDot coded MPC (PolyDot-CMPC) by employing recently proposed coded computation algorithms; MatDot and PolyDot. We take advantage of the ``garbage terms'' that naturally arise when polynomials are constructed in the design of MatDot-CMPC and PolyDot-CMPC to reduce the number of workers needed for privacy-preserving computation. Also, we analyze MatDot-CMPC and PolyDot-CMPC in terms of their computation, storage, communication overhead as well as recovery threshold, so they can easily adapt to the limited resources of edge devices.

中文翻译：

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边缘网络的编码隐私保护计算

多方计算 (MPC) 有望用于边缘网络的隐私保护机器学习算法，例如联邦学习。尽管具有潜力，但现有的 MPC 算法无法适应边缘设备的有限资源。一个有前途的解决方案，也是这项工作的重点，是编码计算，它提倡使用纠错码通过“智能”数据冗余来提高分布式计算的性能。在本文中，我们专注于使用 Shamir 的秘密共享进行编码的隐私保护计算。特别是，我们设计了新颖的编码隐私保护计算机制；MatDot编码的MPC（MatDot-CMPC）和PolyDot编码的MPC（PolyDot-CMPC）采用最近提出的编码计算算法；MatDot 和 PolyDot。我们利用“垃圾术语” 当在 MatDot-CMPC 和 PolyDot-CMPC 的设计中构建多项式以减少隐私保护计算所需的工作人员数量时，自然会出现这种情况。此外，我们从计算、存储、通信开销以及恢复阈值方面分析了 MatDot-CMPC 和 PolyDot-CMPC，因此它们可以轻松适应边缘设备的有限资源。