We consider distributed machine learning (ML) through unmanned aerial vehicles (UAVs) for geo-distributed device clusters. We propose five new technologies/techniques: (i) stratified UAV swarms with leader, worker, and coordinator UAVs, (ii) hierarchical nested personalized federated learning (HN-PFL): a holistic distributed ML framework for personalized model training across the worker-leader-core network hierarchy, (iii) cooperative UAV resource pooling for distributed ML using the UAVs' local computational capabilities, (iv) aerial data caching and relaying for efficient data relaying to conduct ML, and (v) concept/model drift, capturing online data variations at the devices. We split the UAV-enabled model training problem as two parts. (a) Network-aware HN-PFL, where we optimize a tradeoff between energy consumption and ML model performance by configuring data offloading among devices-UAVs and UAV-UAVs, UAVs' CPU frequencies, and mini-batch sizes subject to communication/computation network heterogeneity. We tackle this optimization problem via the method of posynomial condensation and propose a distributed algorithm with a performance guarantee. (b) Macro-trajectory and learning duration design, which we formulate as a sequential decision making problem, tackled via deep reinforcement learning. Our simulations demonstrate the superiority of our methodology with regards to the distributed ML performance, the optimization of network resources, and the swarm trajectory efficiency.

翻译：我们考虑通过无人驾驶飞行器(无人驾驶飞行器)为地理分布式设备集群提供分布式机器学习(ML),我们提出五种新技术/技术:(一) 与领导、工人和协调员无人驾驶飞行器进行分流的无人驾驶飞行器(UAVs)群集;(二) 与领导、工人和协调员进行分层的无人驾驶飞行器(UAVs)群集;(二) 将个人化个人化联合学习(HN-PFL)分为等级的固定型个人化个人化学习(HN-PFL):在工人-领导-核心网络的层次上,为个人化模式培训提供整体分布式计算机学习(MLL)框架;(三) 利用无人驾驶飞行器(UAVs)当地计算能力,为高效数据中继传输ML(AVs)的空中数据存储和中继数据传输(MUAVs),为概念/模型流流流流,在设备中进行通信/流传性能分析。