Overview and Prospect of secure communication based on Federated learning
Qin Chen, Nan Xu · Journal of Physics Conference Series · 2020
Abstract With the increasing amount of data, the in-depth application guided by data prediction has ushered in the climax of development However, due to the limited depth and breadth of the data owned by each unit, the data among different units are not common, so more and more data islands are formed in the city. Data sharing has become a major obstacle to the construction of smart city. To solve the problem of data sharing, in 2016, Google first proposed the concept of Federated learning. However, the bandwidth of wireless communication has not increased significantly. As a result, the bottleneck has shifted from previous computing to today’s communication problems. During my internship in a company, I participated in a project to provide platform support for federal learning. The purpose of the platform is to facilitate the transformation of federal learning academic achievements into commercial products. Based on this project and the current situation of federal learning, this paper summarizes the secure communication problems of federal learning.