Enhancing PLS in UAV-Aided Backscatter Networks: A Multiagent DRL Framework with Federated Learning

This research proposes a framework to improve physical layer security (PLS, the protection of wireless communications at the hardware level) in UAV-aided backscatter networks, which are systems where drones help passive devices transmit data efficiently. The framework uses federated learning (FL, a technique where multiple agents learn from data without sharing raw information) combined with deep reinforcement learning (DRL, machine learning where agents learn by trial and error to make optimal decisions) to optimize how a UAV positions itself, allocates power, and manages a backscatter tag while protecting against eavesdroppers by injecting artificial noise.