In this article, we address the problem of federated learning in the presence
of stragglers. For this problem, a coded federated learning framework has been
proposed, where the central server aggregates gradients received from the
non-stragglers and gradient computed from a privacy-preservation global coded
dataset to mitigate the negative impact of the stragglers. However, when
aggregating these gradients, fixed weights are consistently applied across
iterations, neglecting the generation process of the global coded dataset and
the dynamic nature of the trained model over iterations. This oversight may
result in diminished learning performance. To overcome this drawback, we
propose a new method named adaptive coded federated learning (ACFL). In ACFL,
before the training, each device uploads a coded local dataset with additive
noise to the central server to generate a global coded dataset under privacy
preservation requirements. During each iteration of the training, the central
server aggregates the gradients received from the non-stragglers and the
gradient computed from the global coded dataset, where an adaptive policy for
varying the aggregation weights is designed. Under this policy, we optimize the
performance in terms of privacy and learning, where the learning performance is
analyzed through convergence analysis and the privacy performance is
characterized via mutual information differential privacy. Finally, we perform
simulations to demonstrate the superiority of ACFL compared with the
non-adaptive methods.

本文介绍了自适应编码联邦学习（ACFL）的方法，其中在训练之前，每个设备上传一个带有加性噪声的编码本地数据集到中央服务器，以满足隐私保护要求产生全局编码数据集，通过设计聚合权重的自适应策略，聚合来自非滞后者的梯度和来自全局编码数据集的梯度，在隐私和学习性能优化方面进行性能分析，并进行模拟实验证明 ACFL 的优越性。

自适应编码联邦学习：隐私保护与降低掉队者影响

Adaptive Coded Federated Learning: Privacy Preservation and Straggler  Mitigation

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。

联邦学习中减轻落后者影响的编码

Coding for Straggler Mitigation in Federated Learning

Federated Learning (FL) is an exciting new paradigm that enables training a
global model from data generated locally at the client nodes, without moving
client data to a centralized server. Performance of FL in a multi-access edge
computing (MEC) network suffers from slow convergence due to heterogeneity and
stochastic fluctuations in compute power and communication link qualities
across clients. A recent work, Coded Federated Learning (CFL), proposes to
mitigate stragglers and speed up training for linear regression tasks by
assigning redundant computations at the MEC server. Coding redundancy in CFL is
computed by exploiting statistical properties of compute and communication
delays. We develop CodedFedL that addresses the difficult task of extending CFL
to distributed non-linear regression and classification problems with
multioutput labels. The key innovation of our work is to exploit distributed
kernel embedding using random Fourier features that transforms the training
task into distributed linear regression. We provide an analytical solution for
load allocation, and demonstrate significant performance gains for CodedFedL
through experiments over benchmark datasets using practical network parameters.

CodedFedL extends CFL to distributed non-linear regression and classification problems with multioutput labels by exploiting distributed kernel embedding using random Fourier features that transforms the training task into distributed linear regression, providing an analytical solution for load allocation and significant performance gains over benchmark datasets using practical network parameters.