论文标题
深度神经网络通信的趋势和进步
Trends and Advancements in Deep Neural Network Communication
论文作者
论文摘要
由于其出色的性能和可伸缩性属性，神经网络已成为许多应用程序的无处不在的基础。随着移动和物联网的兴起，这些模型现在也越来越多地应用于分布式设置中，在分布式设置中，数据的所有者被有限的通信渠道和隐私约束所分开。为了应对这些分布式环境的挑战，已经开发了广泛的培训和评估方案，这些方案需要神经网络参数化的交流。这些新颖的方法将“智能到数据”带来的方法比传统云解决方案（例如隐私保护，安全性和设备自主权，沟通效率和高训练速度）具有许多优势。本文概述了机器学习与通信在这一新的研究领域的最新进步和挑战。
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.
