Edge Brain Computing: A Cloud--Edge Framework for Large Brain Foundation Models in Human-Centric IIoT

Abstract

The Intelligent Internet of Things (IIoT) is transitioning from a data-centric to a human-centric paradigm, creating an urgent demand for reliable human–machine interaction. While transformer-based brain foundation models have emerged to decode human intentions, most existing studies focus on improving performance for individual tasks on a single device, and the deployment in real-world IIoT scenarios remains largely unexplored. Specifically, there are three primary challenges for deployment in IIoT: deployment on resource-constrained edge devices, efficient cloud–edge collaborative scheduling, and online update for new users. To address these challenges, this study introduces the edge brain computing (EBC) framework. The framework consists of three key components: 1) a hierarchical cloud–edge split decoding architecture; 2) a game theory-based dynamic self-supervised distillation strategy; and 3) an online updating mechanism to meet the requirements for deployment in IIoT. The experimental results demonstrate that EBC achieves a 97.88% reduction in model parameters and outperforms centralized deployment strategies in inference latency, power consumption, and communication cost, providing a robust pathway for deployment of brain foundation models in human-centric IIoT.