This paper presents a novel approach to enhance the communication efficiency
of federated learning (FL) in multiple input and multiple output (MIMO)
wireless systems. The proposed method centers on a low-rank matrix
factorization strategy for local gradient compression based on alternating
least squares, along with over-the-air computation and error feedback. The
proposed protocol, termed over-the-air low-rank compression (Ota-LC), is
demonstrated to have lower computation cost and lower communication overhead as
compared to existing benchmarks while guaranteeing the same inference
performance. As an example, when targeting a test accuracy of 80% on the
Cifar-10 dataset, Ota-LC achieves a reduction in total communication costs of
at least 30% when contrasted with benchmark schemes, while also reducing the
computational complexity order by a factor equal to the sum of the dimension of
the gradients.

本文提出了一种用于增强多输入多输出（MIMO）无线系统中联邦学习（FL）通信效率的新方法。该方法基于交替最小二乘算法，利用低秩矩阵分解策略对本地梯度压缩进行过空中计算和误差反馈。所提出的协议被称为空中低秩压缩（Ota-LC），在保证相同推理性能的同时，相比现有基准方案具有更低的计算成本和通信开销。以 Cifar-10 数据集为例，当目标测试准确度为 80% 时，Ota-LC 相比基准方案至少可以降低 30% 的总通信开销，并将计算复杂度的阶数降低到梯度维度之和的倍数。

基于误差反馈的低秩梯度压缩技术在 MIMO 无线联邦学习中的应用

Low-Rank Gradient Compression with Error Feedback for MIMO Wireless  Federated Learning

Deep unfolding is a method of growing popularity that fuses iterative
optimization algorithms with tools from neural networks to efficiently solve a
range of tasks in machine learning, signal and image processing, and
communication systems. This survey summarizes the principle of deep unfolding
and discusses its recent use for communication systems with focus on detection
and precoding in multi-antenna (MIMO) wireless systems and belief propagation
decoding of error-correcting codes. To showcase the efficacy and generality of
deep unfolding, we describe a range of other tasks relevant to communication
systems that can be solved using this emerging paradigm. We conclude the survey
by outlining a list of open research problems and future research directions.

本文介绍了一种名为 deep unfolding 的方法，它将迭代优化算法和神经网络工具相结合，可以有效解决机器学习、信号处理、图像处理和通信系统等一系列任务。此次调查重点讨论了在多天线（MIMO）无线系统中，信号检测和预编码以及纠错码的置信传播解码等领域中应用深度展开的情况。实验表明，这种新兴的范式在通信系统中有着显著的效果和通用性。最后，我们总结了一些尚待进一步研究和未来研究方向。