DNN Task Partitioning and Migration Strategies in Multi-UAV-Assisted Mobile Edge Computing

Deep neural networks (DNNs) have been widely applied in mobile intelligent applications. However, their high computational complexity poses significant challenges for resource-constrained mobile devices. To address this issue, this paper proposes a multi-uncrewed aerial vehicle (UAV)-assisted mobile edge computing architecture tailored for DNN inference tasks. By hierarchically partitioning the DNN model and distributing different sub-tasks between local devices and aerial servers for collaborative processing, the system effectively reduces the computational burden on user terminals. Taking into account the factors such as unbalanced network load and limited UAV energy, a task migration mechanism is introduced to support resource coordination and load balancing among multiple UAVs. The aim is to minimize total weighted energy consumption through joint optimization of user-UAV association, DNN partitioning, UAV trajectory, task migration, and computing resource allocation. Due to the dynamic and complex nature of the resulting optimization problem, we model it as a Markov decision process, and a soft actor-critic with prioritized experience replay (SAC-PER) is proposed to solve it. Furthermore, we integrate convex optimization techniques into SAC-PER as a subroutine to allocate computing resources to enhance the learning efficiency. Simulation results show that the proposed method achieves faster convergence and reduces the total weighted energy consumption by up to 14.6% compared with baseline methods.