Scheduling Policies for Federated Learning in Wireless Networks An Overview
Release Date：2020-07-22 Author：SHI Wenqi, SUN Yuxuan, HUANG Xiufeng, ZHOU Sheng, NIU Zhisheng Click：
Scheduling Policies for Federated Learning in Wireless Networks: An Overview
SHI Wenqi, SUN Yuxuan, HUANG Xiufeng, ZHOU Sheng, NIU Zhisheng
(Department of Electronic Engineering, Tsinghua University, Beijing 100084, China)
Due to the increasing need for massive data analysis and machine learning model training at the network edge, as well as the rising concerns about the data privacy, a new distributed training framework called federated learning (FL) has emerged and attracted much attention from both academia and industry. In FL, participating devices iteratively update the local models based on their own data and contribute to the global training by uploading the model updates until the training converges. Therefore, the computation capabilities of mobile devices can be utilized and the data privacy can be preserved. However, deploying FL in resource-constrained wireless networks encounters several challenges, including the limited energy of mobile devices, weak onboard computing capability, and scarce wireless bandwidth. To address these challenges, recent solutions have been proposed to maximize the convergence rate or minimize the energy consumption under heterogeneous constraints. In this overview, we first introduce the backgrounds and fundamentals of FL. Then, the key challenges in deploying FL in wireless networks are discussed, and several existing solutions are reviewed. Finally, we highlight the open issues and future research directions in FL scheduling.
federated learning; wireless network; edge computing; scheduling