We investigate training machine learning (ML) models across a set of
geo-distributed, resource-constrained clusters of devices through unmanned
aerial vehicles (UAV) swarms. The presence of time-varying data heterogeneity
and computational resource inadequacy among device clusters motivate four key
parts of our methodology: (i) stratified UAV swarms of leader, worker, and
coordinator UAVs, (ii) hierarchical nested personalized federated learning
(HN-PFL), a distributed ML framework for personalized model training across the
worker-leader-core network hierarchy, (iii) cooperative UAV resource pooling to
address computational inadequacy of devices by conducting model training among
the UAV swarms, and (iv) model/concept drift to model time-varying data
distributions. In doing so, we consider both micro (i.e., UAV-level) and macro
(i.e., swarm-level) system design. At the micro-level, we propose network-aware
HN-PFL, where we distributively orchestrate UAVs inside swarms to optimize
energy consumption and ML model performance with performance guarantees. At the
macro-level, we focus on swarm trajectory and learning duration design, which
we formulate as a sequential decision making problem tackled via deep
reinforcement learning. Our simulations demonstrate the improvements achieved
by our methodology in terms of ML performance, network resource savings, and
swarm trajectory efficiency.

研究使用无人机队列跨越一组地理分散、资源受限的设备集来训练机器学习模型；其中，关于设备群集的时间变化数据异构性和计算资源不足性，推动了我们方法的四个关键部分：（i）分层的领导机、工作机和协调机无人机群体、（ii）个性化联合学习 (HN-PFL) 的分层嵌套，一种跨工作 - 领导 - 核心网络层次的分布式机器学习框架，（iii）通过在无人机群体之间进行模型训练，合作无人机资源汇集以解决设备的计算不足问题，（iv）模型 / 概念漂移来对时间变化的数据分布进行建模，同时考虑微观（即无人机级别）和宏观（即群体级别）系统设计，微观水平上，我们提出了网络感知 HN-PFL，在该方案中，我们在群体内分布式地编排无人机，以优化能耗和机器学习模型性能，并具有性能保证。 在宏观水平上，我们关注群体轨迹和学习持续时间设计，将其作为顺序决策问题并通过深度强化学习来解决。通过模拟，证明了我们方法在机器学习性能、网络资源节约和群体轨迹效率方面的改进。