<ul><li>This paper introduces a GNN-based multi-node collaborative perception mechanism to address limitations in distributed systems.</li><li>The system is modeled as a graph structure with message-passing and state-update modules.</li><li>A multi-layer graph neural network facilitates efficient information aggregation and dynamic state inference among nodes.</li><li>Experimental results demonstrate that the proposed method improves task completion rate, latency, load balancing, and transmission efficiency in distributed systems.</li></ul>

Graph Neural Network-Based Collaborative Perception for Adaptive Scheduling in Distributed Systems

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