Distributed Learning in Wireless Networks: Recent Progress and Future Challenges
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges
复制标题
DOI：
10.1109/jsac.2021.3118346
复制
发表时间：
2021-12-01
影响因子：
16.4
通讯作者：
Poor, H. Vincent
中科院分区：
文献类型：
--
作者：
Chen, Mingzhe;Gunduz, Deniz;Poor, H. Vincent
下一代无线网络将使许多机器学习（ML）工具和应用程序能够有效地分析边缘设备收集的各种类型的数据，用于推理、自主和决策目的。然而，由于资源限制、延迟限制和隐私挑战，边缘设备无法将其收集的整个数据集卸载到云服务器上，以便集中训练其ML模型或进行推理。为了克服这些挑战，分布式学习和推理技术被提出作为一种手段，使边缘设备能够在没有原始数据交换的情况下协同训练机器学习模型，从而减少通信开销和延迟，并改善数据隐私。然而，在无线网络上部署分布式学习面临着一些挑战，包括不确定的无线环境（例如，动态信道和干扰）、有限的无线资源（例如，发射功率和无线电频谱）和硬件资源（例如，计算能力）。本文全面研究了如何在无线边缘网络上高效部署分布式学习。我们详细概述了几种新兴的分布式学习范式，包括联邦学习、联邦蒸馏、分布式推理和多智能体强化学习。对于每个学习框架，我们首先介绍在无线网络上部署它的动机。然后，我们提出了详细的文献综述使用通信技术的有效部署。然后，我们介绍了一个说明性示例来展示如何优化无线网络以提高其性能。最后介绍了未来的研究机会。简而言之，本文提供了一套关于如何在现实世界的无线通信网络上部署广泛的分布式学习框架的全面指导方针。
The next-generation of wireless networks will enable many machine learning (ML) tools and applications to efficiently analyze various types of data collected by edge devices for inference, autonomy, and decision making purposes. However, due to resource constraints, delay limitations, and privacy challenges, edge devices cannot offload their entire collected datasets to a cloud server for centrally training their ML models or inference purposes. To overcome these challenges, distributed learning and inference techniques have been proposed as a means to enable edge devices to collaboratively train ML models without raw data exchanges, thus reducing the communication overhead and latency as well as improving data privacy. However, deploying distributed learning over wireless networks faces several challenges including the uncertain wireless environment (e.g., dynamic channel and interference), limited wireless resources (e.g., transmit power and radio spectrum), and hardware resources (e.g., computational power). This paper provides a comprehensive study of how distributed learning can be efficiently and effectively deployed over wireless edge networks. We present a detailed overview of several emerging distributed learning paradigms, including federated learning, federated distillation, distributed inference, and multi-agent reinforcement learning. For each learning framework, we first introduce the motivation for deploying it over wireless networks. Then, we present a detailed literature review on the use of communication techniques for its efficient deployment. We then introduce an illustrative example to show how to optimize wireless networks to improve its performance. Finally, we introduce future research opportunities. In a nutshell, this paper provides a holistic set of guidelines on how to deploy a broad range of distributed learning frameworks over real-world wireless communication networks.