Peng, Yubo and Jiang, Feibo and Tu, Siwei and Dong, Li and Wang, Kezhi and Yang, Kun (2024) Dynamic Client Scheduling Enhanced Federated Learning for UAVs. IEEE Wireless Communications Letters, 13 (7). pp. 1998-2002. DOI https://doi.org/10.1109/lwc.2024.3400813
Peng, Yubo and Jiang, Feibo and Tu, Siwei and Dong, Li and Wang, Kezhi and Yang, Kun (2024) Dynamic Client Scheduling Enhanced Federated Learning for UAVs. IEEE Wireless Communications Letters, 13 (7). pp. 1998-2002. DOI https://doi.org/10.1109/lwc.2024.3400813
Peng, Yubo and Jiang, Feibo and Tu, Siwei and Dong, Li and Wang, Kezhi and Yang, Kun (2024) Dynamic Client Scheduling Enhanced Federated Learning for UAVs. IEEE Wireless Communications Letters, 13 (7). pp. 1998-2002. DOI https://doi.org/10.1109/lwc.2024.3400813
Abstract
Although Federated Learning (FL) applied in Unmanned Aerial Vehicles (UAVs) offers substantial benefits, it also poses some challenges. These challenges arise primarily from the dynamic nature of UAV movements and the constraints imposed by limited wireless channel resources. This leads to the situation where only partial UAVs can participate in the FL process during each communication round, introducing the bias of the optimization objective that adversely impacts model accuracy. To address this issue, we introduce a Multi-action Q Network (MQN) for client scheduling, which selects suitable UAVs for each round, resolving the problems of the partial participation of UAVs. Furthermore, we propose a Gain-based Parameter Aggregation (GPA), which assigns a “contribution score" to each local model based on its contribution, correcting the bias of the optimization objective in FL. Simulation results demonstrate the effectiveness of the proposed methods.
Item Type: | Article |
---|---|
Uncontrolled Keywords: | Federated learning; deep reinforcement learning; wireless communications; unmanned aerial vehicles |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 08 Jul 2024 09:15 |
Last Modified: | 22 Jul 2024 15:51 |
URI: | http://repository.essex.ac.uk/id/eprint/38736 |
Available files
Filename: Accepted_Manuscript.pdf