  Computational offloading has become an enabling component for edge
intelligence in mobile and smart devices. Existing offloading schemes mainly
focus on mobile devices and servers, while ignoring the potential network
congestion caused by tasks from multiple mobile devices, especially in wireless
multi-hop networks. To fill this gap, we propose a low-overhead,
congestion-aware distributed task offloading scheme by augmenting a distributed
greedy framework with graph-based machine learning. In simulated wireless
multi-hop networks with 20-110 nodes and a resource allocation scheme based on
shortest path routing and contention-based link scheduling, our approach is
demonstrated to be effective in reducing congestion or unstable queues under
the context-agnostic baseline, while improving the execution latency over local
computing.
