Unmanned Aerial Vehicles (UAVs) are a rapidly emerging technology offering fast and cost-effective solutions for many areas, including public safety, surveillance, and wireless networks. However, due to the highly dynamic network topology of UAVs, traditional mesh networking protocols, such as the Better Approach to Mobile Ad-hoc Networking (B.A.T.M.A.N.), are unsuitable. To this end, we investigate modifying the B.A.T.M.A.N. routing protocol with a machine learning (ML) model and propose implementing this solution using federated learning (FL). This work aims to aid the routing protocol to learn to predict future network topologies and preemptively make routing decisions to minimize network congestion. We also present an FL testbed built on a network emulator for future testing of the proposed ML aided B.A.T.M.A.N. routing protocol.

翻译：无人机（UAVs）作为一种快速发展的新兴技术，为公共安全、监控和无线网络等领域提供了快速且经济的解决方案。然而，由于无人机网络拓扑的高度动态性，传统网状网络协议（如更优移动自组网方式B.A.T.M.A.N.）并不适用。为此，我们研究通过机器学习（ML）模型改进B.A.T.M.A.N.路由协议，并提出利用联邦学习（FL）实现该方案。本工作旨在辅助路由协议学习预测未来网络拓扑，并预先做出路由决策以最小化网络拥塞。同时，我们搭建了一个基于网络模拟器的联邦学习测试平台，用于未来对基于机器学习的B.A.T.M.A.N.路由协议进行测试。