通过通信压缩和能量分配来帮助无人机群体的联合学习延迟最小化方法
Liang Zeng1, Wenxin Wang2, Wei Zuo3
1School of Cyberspace Science and Technology, Beijing Institute of Technology, No. 5 Zhongguancun South Street, Beijing 100081, China.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
在无人机飞行器群 (UAVS) 中的联合学习 (FL) 被优化为延迟敏感任务. 一个高效的异步联合学习机制与殖民地优化减少通信时间,同时保持准确性.
科学领域:
- 机器人技术和自主系统
- 机器学习 机器学习
- 网络计算 网络计算 网络计算
背景情况:
- 无人驾驶飞行器群 (UAVS) 利用机器学习 (ML) 进行检测和绘图等任务.
- 联合学习 (FL) 适用于UAVS,因为与地面站的连接挑战.
- 对于延迟敏感的操作,例如紧急避开障碍,在UAVS中对FL构成挑战.
研究的目的:
- 在UAVS中分析FL的能量消耗和延迟灵敏度.
- 建议在延迟关键的无人机应用中优化FL的解决方案.
- 为了增强UAV群体的边缘网络计算.
主要方法:
- 为边缘网络计算 (EAFLM) 开发了一个高效的异步联合学习机制.
- 与EAFLM集成的群优化 (ACO),以管理无人机参与和资源分配.
- 屏蔽无人机进行通信,并调整发射功率和CPU频率以实现更快的代.
主要成果:
- 显著减少了UAVS之间的通信时间.
- 证明对模型准确性的影响相对较小.
- 优化了无人机通信资源的有效分配.
- 使用MNIST数据集验证了拟议的方法.
结论:
- 拟议的EAFLM与ACO相结合,为UAVS中的延迟敏感FL任务提供了有效的解决方案.
- 这种方法平衡了通信效率,能源消耗和计算性能.
- 它增强了FL在动态无人机群作战中的实际应用性.
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