It is anticipated that aerial-terrestrial integrated networks incorporating unmanned aerial vehicles (UAVs) mounted relays will offer improved coverage and connectivity in the beyond 5G era. Meanwhile, federated learning (FL) is a promising distributed machine learning technique for building inference models over wireless networks due to its ability to maintain user privacy and reduce communication overhead. However, off-the-shelf FL models aggregate global parameters at a central parameter server (CPS), increasing energy consumption and latency, as well as inefficiently utilizing radio resource blocks (RRBs) for distributed user devices (UDs). This paper presents a resource-efficient FL framework, called FedMoD (\textbf{fed}erated learning with \textbf{mo}del \textbf{d}issemination), for millimeter-wave (mmWave) aerial-terrestrial integrated networks with the following two unique characteristics. Firstly, FedMoD presents a novel decentralized model dissemination algorithm that makes use of UAVs as local model aggregators through UAV-to-UAV and device-to-device (D2D) communications. As a result, FedMoD (i) increases the number of participant UDs in developing FL model and (ii) achieves global model aggregation without involving CPS. Secondly, FedMoD reduces the energy consumption of FL using radio resource management (RRM) under the constraints of over-the-air learning latency. In order to achieve this, by leveraging graph theory, FedMoD optimizes the scheduling of line-of-sight (LOS) UDs to suitable UAVs/RRBs over mmWave links and non-LOS UDs to available LOS UDs via overlay D2D communications. Extensive simulations reveal that decentralized FedMoD offers same convergence rate performance as compared to conventional FL frameworks.

翻译：本文提出了一种面向毫米波空地一体化网络的高效资源联邦学习框架FedMoD（联邦学习与模型分发）。该框架具有以下两个独特特性：首先，FedMoD提出了一种基于无人机之间（UAV-to-UAV）和设备之间（D2D）通信的新型去中心化模型分发算法，将无人机作为本地模型聚合器。由此，FedMoD（i）扩大了参与联邦学习模型开发的用户设备数量，且（ii）无需中央参数服务器即可实现全局模型聚合。其次，FedMoD在无线学习时延约束下，通过无线资源管理降低了联邦学习的能耗。为实现此目标，借助图论，FedMoD优化了视距用户设备与毫米波链路上合适无人机/资源块的调度，并通过覆盖D2D通信将非视距用户设备与可用视距用户设备连接。仿真结果表明，与传统的联邦学习框架相比，去中心化的FedMoD在收敛速率性能上表现相当。