标题： 深度神经网络通信中的趋势和进展 显示英文标题

标题： Trends and Advancements in Deep Neural Network Communication

摘要： 由于神经网络在性能和可扩展性方面的出色表现，它们已成为许多应用的通用构建块。 随着移动设备和物联网的兴起，这些模型现在也被越来越多地应用于分布式环境，在这种环境中，数据的所有者通过有限的通信通道和隐私限制分隔开来。 为了解决这些分布式环境中的挑战，已经开发出一系列训练和评估方案，这些方案需要通信神经网络的参数化。 这些将“智能带到数据中”的新方法相较于传统的云解决方案具有许多优势，例如隐私保护、增强的安全性、设备自主性、通信效率和高效的训练速度。 本文综述了机器学习与通信交叉领域这一新兴研究方向的最新进展和挑战。 显示英文摘要

摘要： Due to their great performance and scalability properties neural networks have become ubiquitous building blocks of many applications. With the rise of mobile and IoT, these models now are also being increasingly applied in distributed settings, where the owners of the data are separated by limited communication channels and privacy constraints. To address the challenges of these distributed environments, a wide range of training and evaluation schemes have been developed, which require the communication of neural network parametrizations. These novel approaches, which bring the "intelligence to the data" have many advantages over traditional cloud solutions such as privacy-preservation, increased security and device autonomy, communication efficiency and high training speed. This paper gives an overview over the recent advancements and challenges in this new field of research at the intersection of machine learning and communications.