Research Article
Federated Learning Based User Scheduling for Real-Time Multimedia Tasks in Edge Devices
@INPROCEEDINGS{10.1007/978-3-031-28990-3_19, author={Wenkan Wen and Yiwen Liu and Yanxia Gao and Zhirong Zhu and Yuanquan Shi and Xiaoning Peng}, title={Federated Learning Based User Scheduling for Real-Time Multimedia Tasks in Edge Devices}, proceedings={Edge Computing and IoT: Systems, Management and Security. Third EAI International Conference, ICECI 2022, Virtual Event, December 13-14, 2022, Proceedings}, proceedings_a={ICECI}, year={2023}, month={3}, keywords={Federated Learning User Scheduling Mobile Computing Machine learning}, doi={10.1007/978-3-031-28990-3_19} }- Wenkan Wen
Yiwen Liu
Yanxia Gao
Zhirong Zhu
Yuanquan Shi
Xiaoning Peng
Year: 2023
Federated Learning Based User Scheduling for Real-Time Multimedia Tasks in Edge Devices
ICECI
Springer
DOI: 10.1007/978-3-031-28990-3_19
Abstract
Edge networks are highly volatile and the quality of device communication and computational resources change not only over time but also according to the movement of users. Current federation learning suffers from poor device network state and failure of devices to upload models in a timely manner. To address these problems, an intelligent scheduling mechanism that uses the predicted device state based on device information to select the appropriate device for federated learning is proposed in this paper. By focusing on information such as communication quality, computational resources, and location information, the information of edge devices is collected to analyze and predict the device network and computing resources to further analyze the state of devices in depth. Experiments are conducted on real datasets, and the experimental results show that the proposed scheduling method can make the global model fit faster than without the algorithm, which significantly improves the training efficiency of federated learning.
