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

发表刊物：WASA 2022

年份：November 2022

摘要：Federated Learning (FL) is a distributed machine learning approach which is suitable for edge computing environment. While in this environment, how to take full advantage of the computing resources on end devices and edge servers is still a difficult problem. Especially for the synchronous federated learning, computing resources among different participants will lead to extra time cost and cause resource waste. In this paper, we try to reduce the time cost and the computing resource waste by using model splitting and task scheduling. We first establish the mathematical model and find it can not be solved directly. Then we design our algorithm which we name as the Federated Learning Offloading Acceleration (FLOA) algorithm to obtain a sub-optimal solution. The FLOA algorithm first uses the Partition Points Selection method to reduce the size of the solution space, then proposes a task offloading method based on matching theory. Experiments and simulations show that compared to the other three calculation methods, the single iteration time is reduced by 47%, 28%, 14% under our algorithm in turn.

参考文献拷贝字段：Chen Fang, Lei Shi, Yi Shi, Jing Xu, Xu Ding. Synchronous Federated Learning Latency Optimization Based on Model Splitting [C]. The 17th International Conference on Wireless Algorithms, Systems, and Applications (WASA), Dalian, China, November 17-19, 2022: 495-506