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Title: | Dynamic Client Scheduling Enhanced Federated Learning for UAVs |
Authors: | Peng, Y Jiang, F Tu, S Dong, L Wang, K Yang, K |
Keywords: | federated learning;deep reinforcement learning;wireless communications;unmanned aerial vehicles |
Issue Date: | 14-May-2024 |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Citation: | Peng, Y. et al. (2024) 'Dynamic Client Scheduling Enhanced Federated Learning for UAVs', IEEE Wireless Communications Letters, 0 (early access), pp. 1 - 5. doi: 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. |
URI: | https://bura.brunel.ac.uk/handle/2438/29158 |
DOI: | https://doi.org/10.1109/LWC.2024.3400813 |
ISSN: | 2162-2337 |
Other Identifiers: | ORCiD: Yubo Peng https://orcid.org/0000-0001-9684-2971 ORCiD: Feibo Jiang https://orcid.org/0000-0002-0235-0253 ORCiD: Li Dong https://orcid.org/0000-0002-0127-8480 ORCiD: Kezhi Wang https://orcid.org/0000-0001-8602-0800 ORCiD: Kun Yang https://orcid.org/0000-0002-6782-6689 |
Appears in Collections: | Dept of Computer Science Research Papers |
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