Multi-Agent Meta Reinforcement Learning for Reliable and Low-Latency Distributed Inference in Resource-Constrained UAV Swarms

The integration of unmanned aerial vehicles (UAVs) in the Industrial Internet of Things (IIoT) for smart city applications has been gaining significant attention. UAV swarms are increasingly employed to monitor ground-based IIoT devices in smart cities, offering valuable support to situation-awareness IoT applications, such as surveillance, traffic management, and emergency response. A key requirement in these applications is minimizing the latency of data processing, particularly for time-sensitive tasks like image classification of IIoT device data. Due to resource limitations, UAVs often rely on online task offloading to remote machines, but this can be inefficient due to unstable connections, constrained resources, and high latency. Distributed inference enabled via swarms of collaborative UAVs presents a promising solution by partitioning tasks among UAVs based on their available resources, allowing for more efficient, collaborative processing. However, the IIoT inference distribution raises challenges in ensuring reliable data transmission with minimal latency while respecting the practical UAVs’ constraints. To address these issues, we formulate the problem of CNN layer distribution and UAV trajectory planning (LDTP) as an optimization problem to improve latency, reliability, and resource usage. Given the complexity of the LDTP solution for managing online requests, we propose a real-time, lightweight solution using multi-agent meta-reinforcement learning. Our approach is tested on CNN networks and benchmarked against state-of-the-art conventional reinforcement learning algorithms. Extensive simulations show that our model outperforms competitive methods by around 29% in terms of latency and around 23% in terms of transmission power improvements while delivering results comparable to the traditional LDTP optimization solution by around 9% in terms of latency.