A Meta-Computing Framework for Collaborative Federated Graph Learning in Industrial IoT

Owing to strong capabilities in capturing interactions among objects and concepts, graph data has been treated as an important type of information collected by smart devices in Industrial Internet of Things (IoT), and the distributed training of graph learning models over these devices brings fundamental supports for intelligent services and operations. However, different IoT devices may collect Non-IID graph data due to different roles in the system, and suffer poor performance when only one unified instance of model is trained. Besides, IoT devices usually belong to different communities in Industrial IoT, such that each community pursues both optimized and rational performance when joining in the training process. Considering both challenges, this article proposes a novel meta-computing framework for federated graph learning in Industrial IoT. A collaborative resource allocation task is formulated where devices belonging to different communities adopt limited resources to participate in the training of multiple instances either within or across communities. Two algorithms are introduced for adaptive and rational resource allocation based on whether devices are owned by single or multiple communities. Both algorithms provide guaranteed performance on efficiency and effectiveness, and the fairness among IoT devices are proved. Finally, extensive numerical results have demonstrated the performance of the proposed framework in handling collaborative graph model learning within Industrial IoT.