Federated Dual Proximal Regularization for energy-efficient decentralized UAV navigation

Federated reinforcement learning (FRL) for unmanned aerial vehicles (UAVs) in mobile edge computing (MEC) typically relies on fixed-strength proximal constraints or Kullback–Leibler (KL) divergence penalties in distribution space, both of which break down when UAVs in a fleet have different rotor disc areas, coverage radii, and maximum accelerations: fixed coefficients cannot adapt across training phases, and KL estimates become noisy when heterogeneous UAVs explore different state regions. FedDPR (Federated Dual Proximal Regularization) replaces the KL penalty with dual L2-norm proximal terms in parameter space for state-independent gradients, and anneals the global coefficient over FL rounds to transition from fleet-wide knowledge sharing to per-UAV hardware specialization. FedDPR outperforms FedProx, FMARL, and FedKL in energy efficiency, average waiting time, and cumulative reward.