Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms
Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms
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DOI：
10.1109/icc40277.2020.9148776
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发表时间：
2020-02
期刊：
影响因子：
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通讯作者：
Tengchan Zeng;Omid Semiari;Mohammad Mozaffari;Mingzhe Chen;W. Saad;M. Bennis
中科院分区：
文献类型：
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作者：
Tengchan Zeng;Omid Semiari;Mohammad Mozaffari;Mingzhe Chen;W. Saad;M. Bennis
无人机群必须利用机器学习（ML）来执行从协调轨迹规划到协同目标识别的各种任务。然而，由于无人机群与地面基站（BS）之间缺乏连续连接，使用集中式ML将具有挑战性，特别是在处理大量数据时。在本文中，提出了一种新的框架内实现分布式联邦学习（FL）算法的无人机群，由一个领先的无人机和几个以下的无人机。每个跟随的UAV基于其收集的数据训练本地FL模型，然后将此训练的本地模型发送到领先的UAV，领先的UAV将聚合接收到的模型，生成全局FL模型，并通过群内网络将其发送到跟随者。为了确定无线因素，如衰落，传输延迟，以及由风和机械振动引起的无人机天线角度偏差，如何影响FL的性能，对FL进行了严格的收敛分析。然后，联合功率分配和调度设计，提出了优化FL的收敛速度，同时考虑到收敛过程中的能量消耗和延迟的要求所施加的群体的控制系统。仿真结果验证了FL收敛性分析的有效性，并表明联合设计策略可以将收敛所需的通信轮数减少多达35%。
Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks ranging from coordinated trajectory planning to cooperative target recognition. However, due to the lack of continuous connections between the UAV swarm and ground base stations (BSs), using centralized ML will be challenging, particularly when dealing with a large volume of data. In this paper, a novel framework is proposed to implement distributed federated learning (FL) algorithms within a UAV swarm that consists of a leading UAV and several following UAVs. Each following UAV trains a local FL model based on its collected data and then sends this trained local model to the leading UAV who will aggregate the received models, generate a global FL model, and transmit it to followers over the intra-swarm network. To identify how wireless factors, like fading, transmission delay, and UAV antenna angle deviations resulting from wind and mechanical vibrations, impact the performance of FL, a rigorous convergence analysis for FL is performed. Then, a joint power allocation and scheduling design is proposed to optimize the convergence rate of FL while taking into account the energy consumption during convergence and the delay requirement imposed by the swarm’s control system. Simulation results validate the effectiveness of the FL convergence analysis and show that the joint design strategy can reduce the number of communication rounds needed for convergence by as much as 35% compared with the baseline design.