A Research on Collaborative UAVs Intelligent Decision Optimization for AoI-driven Federated Learning

FAN Wen; WEI Qian; ZHOU Zhi; YU Shuai; CHEN Xu

doi:10.11999/JEIT211406

Volume 44 Issue 9

Sep. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(9): 2994-3003

FAN Wen, WEI Qian, ZHOU Zhi, YU Shuai, CHEN Xu. A Research on Collaborative UAVs Intelligent Decision Optimization for AoI-driven Federated Learning[J]. Journal of Electronics & Information Technology, 2022, 44(9): 2994-3003. doi: 10.11999/JEIT211406

Citation:

FAN Wen, WEI Qian, ZHOU Zhi, YU Shuai, CHEN Xu. A Research on Collaborative UAVs Intelligent Decision Optimization for AoI-driven Federated Learning[J]. Journal of Electronics & Information Technology, 2022, 44(9): 2994-3003. doi: 10.11999/JEIT211406

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A Research on Collaborative UAVs Intelligent Decision Optimization for AoI-driven Federated Learning

doi: 10.11999/JEIT211406

School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, China

Funds: The National Natural Science Foundation of China (U20A20159, 61972432)

Received Date: 2021-11-30
Accepted Date: 2022-06-01
Rev Recd Date: 2022-05-13

Available Online: 2022-06-07

Publish Date: 2022-09-19

Abstract

Abstract

Federated learning is one of the key technologies of 6G, which can use cross-device data to train a usable and safe sharing model on the premise of protecting data privacy. However, most end devices have limited processing capabilities and can not support complex machine learning model training processes. In the framework of Mobile Edge Computing (MEC) in a heterogeneous network convergence environment, multiple Unmanned Aerial Vehicles (UAVs) are used as aerial edge servers to move flexibly within the target area in a collaborative manner, and collect fresh data in time for federated learning and local training to ensure real-time data learning. Multiple factors, such as data freshness, communication cost and model quality, are considered, and the flight trajectories of UAVs, the communication decisions with the user equipment, and the collaborative work between UAVs are comprehensively optimized. Moreover, a priority-based decomposable multi-agent deep reinforcement learning algorithm is used to solve the continuous online decision-making problem of multiple UAVs federated learning to achieve effective collaboration and control. By using multiple real data sets for simulation experiments, simulation results verify that the proposed algorithm can achieve superior performance under different data distributions and in rapidly changing complex dynamic environments.
- Mobile Edge Computing (MEC),
- Federated learning,
- Deep reinforcement learning,
- Unmanned Aerial Vehicle (UAV),
- Age-of-Information (AoI)

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References(14)

References

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