We present a novel coded federated learning (FL) scheme for linear regression
that mitigates the effect of straggling devices while retaining the privacy
level of conventional FL. The proposed scheme combines one-time padding to
preserve privacy and gradient codes to yield resiliency against stragglers and
consists of two phases. In the first phase, the devices share a one-time padded
version of their local data with a subset of other devices. In the second
phase, the devices and the central server collaboratively and iteratively train
a global linear model using gradient codes on the one-time padded local data.
To apply one-time padding to real data, our scheme exploits a fixed-point
arithmetic representation of the data. Unlike the coded FL scheme recently
introduced by Prakash \emph{et al.}, the proposed scheme maintains the same
level of privacy as conventional FL while achieving a similar training time.
Compared to conventional FL, we show that the proposed scheme achieves a
training speed-up factor of $6.6$ and $9.2$ on the MNIST and Fashion-MNIST
datasets for an accuracy of $95\%$ and $85\%$, respectively.

本文提出了一种新的编码联合学习方案，利用一次性填充保护隐私和梯度编码以增强对抗滞后设备的鲁棒性，通过共享一次性填充局部数据集并使用梯度编码的方式，实现联合迭代训练全局线性模型。相比传统联合学习，该方案在 MNIST 和 Fashion-MNIST 数据集上将训练速度提高了 6.6 和 9.2 倍，在准确率达到 95% 和 85% 时，保持了传统联合学习的隐私保护水平。