A case study about different network architectures in Federated Machine Learning

Modern artificial intelligence (AI) technology is developing rapidly in recent years. Data is an important factor driving the development of AI.With the development of mobile Internet, more and more data is generated in different fields every day along with data sensitivity issues. As asignificant part of personal privacy, personal data must be respected and protected. Federated learning (FL) is a machine learning technology that can protect privacy because it keeps everyone’s data local. Many types of research have already confirmed that the bottleneck of federated learning is the communication between the client and servers. Different ways of communication methods have various characteristics, resulting in differences inefficiency. We present a benchmark for our FL system using HTTP and gRPC communication protocol respectively to show that gRPC framework is faster and has better scalability than HTTP protocol mainly because of the different architectures and way of compacting message of these two different communication protocols. In addition, we found that the system may get crashed when the loads increased. A registration mechanism is proposed to deal with the problem of insufficient computing resources and apply a new model updatestrategy to make the training process finish in a shorter time.Tryckt av