Intelligent Sky Computing: A DDPG‐Based Joint Optimization Scheme for Privacy‐Aware Task Offloading in Multi‐UAV Networks

Exploiting privacy‐aware user task offloading in a multi‐UAV–assisted edge computing system offers a new approach to reduce and balance energy consumption and latency. However, the complexity and variability of operating scenarios can make privacy‐aware user task offloading strategies challenging. This paper examines a system with multiple ground users, UAV servers equipped with computational resources, and a cloud server. Specifically, to implement the optimization algorithm, we first model various behaviors and overheads during the interaction between UAVs and users, including UAV flight, energy consumption, delay, and privacy, and combine these four models to formulate the optimization objective as a minimization problem. Subsequently, a Markov decision process is established for this problem. The UAV flight trajectory, system resource allocation scheme, and user task offloading strategy are jointly optimized using the deep deterministic policy gradient algorithm to solve this minimization problem and determine the optimal task offloading strategy. Finally, simulation experiments demonstrate the convergence performance of the proposed algorithm, verifying its effectiveness in reducing energy consumption and delay while enhancing user privacy protection across different scenarios.