作者：Jing Xu, Lei Shi,Yi Shi，Chen Fang, Juan Xu

发表刊物：WASA 2022

年份：November 2022

摘要：Federated learning based on edge computing environment has great potential to facilitate the implementation of artificial intelligence at the edge of the network. However, because of the limited resource at the edge, place the complete Deep Neural Networks (DNN) model on the edge for training may not a good choice. In this paper, we study the time optimization for asynchronous federated learning based on model partition. That is, the DNN model is divided into two parts and deployed separately on the device and the edge server for the model training. First, we give the metric of the relationship between learning accuracy and iteration frequency, and then we build a mathematical model based on this. Because the solution space of mathematical model is too large to be solved directly, we propose an algorithm to minimize the total time by dynamically adjusting the model partition point and bandwidth allocation. Simulation results show that our algorithm can reduce the time by 32% to 60% compared with the other three methods.

参考文献拷贝字段：Jing Xu, Lei Shi,Yi Shi，Chen Fang, Juan Xu. An Asynchronous Federated Learning Optimization Scheme Based on Model Partition [C]. The 17th International Conference on Wireless Algorithms, Systems, and Applications (WASA), Dalian, China, November 17-19, 2022: 367-379