Decentralized Model Dissemination Empowered Federated Learning in mmWave Aerial-Terrestrial Integrated Networks

Understand

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.