In this work, we investigate the problem of an online trajectory design for an Unmanned Aerial Vehicle (UAV) in a Federated Learning (FL) setting where several different communities exist, each defined by a unique task to be learned. In this setting, spatially distributed devices belonging to each community collaboratively contribute towards training their community model via wireless links provided by the UAV. Accordingly, the UAV acts as a mobile orchestrator coordinating the transmissions and the learning schedule among the devices in each community, intending to accelerate the learning process of all tasks. We propose a heuristic metric as a proxy for the training performance of the different tasks. Capitalizing on this metric, a surrogate objective is defined which enables us to jointly optimize the UAV trajectory and the scheduling of the devices by employing convex optimization techniques and graph theory. The simulations illustrate the out-performance of our solution when compared to other handpicked static and mobile UAV deployment baselines.
翻译:在这项工作中,我们调查了联邦学习联合会(FL)中存在若干不同社区、每个社区都有独特的任务定义的无人驾驶航空飞行器在线轨迹设计问题,在这一环境中,属于每个社区的空间分布装置通过无人驾驶飞行器提供的无线链接共同帮助培训其社区模式。因此,无人驾驶飞行器作为移动管弦,协调每个社区各装置的传输和学习时间表,打算加快所有任务的学习进程。我们提议用超模度作为不同任务培训绩效的代用指标。利用这一指标,确定了一个替代目标,使我们能够通过使用配置优化技术和图形理论,共同优化无人驾驶飞行器的轨迹和装置的时间安排。模拟表明,与其他手选的静电移动无人驾驶飞行器部署基线相比,我们解决方案的绩效已经超出。