UAV-Centric Privacy-Preserving Computation Offloading in Multi-UAV Mobile Edge Computing

Unmanned aerial vehicles (UAVs) offer high mobility, cost-effectiveness and flexible deployment, but their limited computing and battery resources constrain their development. Mobile edge computing (MEC) can alleviate these constraints by computation offloading. Although reinforcement learning (RL) has recently been applied to optimize offloading strategies, using raw UAV data poses a risk of privacy leakage. To address this issue, we design a privacy-preserving RL-based offloading approach that applies local differential privacy (LDP) to perturb decision trajectories. We theoretically derive the O(M/ϵ) regret bound and achieve (ϵ,δ)-LDP for the perturbation mechanism. Finally, we evaluate the efficiency of the proposed approach through experiments.