mlsanityData to 14 Oct 2026

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

arXiv 2505.16248 ↗First posted 22 May 2025cs.LGcited by 4

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Abstract (arXiv)

This paper addresses the limitations of multi-node perception and delayed scheduling response in distributed systems by proposing a GNN-based multi-node collaborative perception mechanism. The system is modeled as a graph structure. Message-passing and state-update modules are introduced. A multi-layer graph neural network is constructed to enable efficient information aggregation and dynamic state inference among nodes. In addition, a perception representation method is designed by fusing local states with global features. This improves each node's ability to perceive the overall system status. The proposed method is evaluated within a customized experimental framework. A dataset featuring heterogeneous task loads and dynamic communication topologies is used. Performance is measured in terms of task completion rate, average latency, load balancing, and transmission efficiency. Experimental results show that the proposed method outperforms mainstream algorithms under various conditions, including limited bandwidth and dynamic structural changes. It demonstrates superior perception capabilities and cooperative scheduling performance. The model achieves rapid convergence and efficient responses to complex system states.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 10 of its 19 references that are in the map. First posted on arXiv 22 May 2025.

Builds on

10 of 19 references.

References · 10

  1. 2022Federated Graph Neural Networks: Overview, Techniques and Challengescited by 52
  2. 2022Distributed Graph Neural Network Training: A Surveycited by 32
  3. 2022A Comprehensive Survey on Distributed Training of Graph Neural Networkscited by 15
  4. 2023The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Surveycited by 14
  5. 2025Dynamic Operating System Scheduling Using Double DQN: A Reinforcement Learning Approach to Task Optimizationcited by 10
  6. 2025Addressing Class Imbalance with Probabilistic Graphical Models and Variational Inferencecited by 9
  7. 2024Machine Learning Techniques for Pattern Recognition in High-Dimensional Data Miningcited by 8
  8. 2025A Deep Learning Framework for Sequence Mining with Bidirectional LSTM and Multi-Scale Attentioncited by 7
  9. 2025State-Aware IoT Scheduling Using Deep Q-Networks and Edge-Based Coordinationcited by 5
  10. 2025Graph-Based Spectral Decomposition for Parameter Coordination in Language Model Fine-Tuningcited by 5

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Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025Autonomous Resource Management in Microservice Systems via Reinforcement Learningcited by 7
  2. 2025Behavioral Anomaly Detection in Distributed Systems via Federated Contrastive Learningcited by 4
  3. 2025Transferable Modeling Strategies for Low-Resource LLM Tasks: A Prompt and Alignment-Based Approachcited by 4
  4. 2025Topology-Aware Graph Reinforcement Learning for Dynamic Routing in Cloud Networkscited by 1