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.
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Acknowledgment
This work was supported in part by the Scientific research projects funded by the Department of education of Hunan Province (No. 22C0497), the Huaihua University Double First-Class initiative Applied Characteristic Discipline of Control Science and Engineering (No. ZNKZN2021-10), the National Natural Science Foundation of China (No. 62172182), the Hunan Provincial Natural Science Foundation of China (No. 2020JJ4490), the Project of Hunan Provincial Social Science Foundation (No. 21JD046), the Huaihua University Project (No. HHUY2019-25), the Philosophy and Social Science Achievement Evaluation Committee of Huaihua (No. HSP2022YB40) and the Science and Technology Innovation 2030 Special Project Sub-Topics (No. 2018AAA0102100).
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Wen, W., Liu, Y., Gao, Y., Zhu, Z., Shi, Y., Peng, X. (2023). Federated Learning Based User Scheduling for Real-Time Multimedia Tasks in Edge Devices. In: **ao, Z., Zhao, P., Dai, X., Shu, J. (eds) Edge Computing and IoT: Systems, Management and Security. ICECI 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 478. Springer, Cham. https://doi.org/10.1007/978-3-031-28990-3_19
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