For the training process of federated linear regression (FLR), which is the simplest form of federated learning, the integrated computation at each company is slowed down either by huge volume data or by time-consuming homomorphic encryption. Targetted at accelerating the training process of FLR, through the incorporation of edge computing aided coded distributed computing (CDC) into intensive computation (matrix multiplication), a novel coded FLR framework is proposed where several edge nodes aid the computing of one company. Two schemes, including linear combination (LC)-based vertical FLR and Matdot-based vertical FLR, are proposed and designed, which enjoy in-parallel computation and homomorphic encryption at the edge nodes. Since workload at each edge node is reduced significantly, the training runtime of these two schemes may be reduced significantly. Numerical studies show that our proposed coded schemes outperform traditional uncoded schemes significantly in terms of overall runtime (sum of encoding, computing, and decoding phases) of the training process. Besides, among the two proposed coded schemes, LC-based scheme and Matdot-based scheme each has its own advantage scenarios which conforms with the analysis.

中文翻译：

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边缘计算辅助编码垂直联合线性回归

对于联邦线性回归 (FLR) 的训练过程，这是联邦学习的最简单形式，每个公司的集成计算都因庞大的数据量或耗时的同态加密而减慢。针对加速FLR的训练过程，通过将边缘计算辅助编码分布式计算（CDC）结合到密集计算（矩阵乘法）中，提出了一种新颖的编码FLR框架，其中多个边缘节点辅助一个公司的计算。提出并设计了基于线性组合（LC）的垂直FLR和基于Matdot的垂直FLR两种方案，在边缘节点享受并行计算和同态加密。由于每个边缘节点的工作量显着减少，这两种方案的训练时间可能会大大减少。数值研究表明，我们提出的编码方案在训练过程的整体运行时间（编码、计算和解码阶段的总和）方面明显优于传统的未编码方案。此外，在提出的两种编码方案中，基于LC的方案和基于Matdot的方案各有其优势场景，符合分析。