Summary of Fully Distributed Online Training Of Graph Neural Networks in Networked Systems, by Rostyslav Olshevskyi et al.
Fully Distributed Online Training of Graph Neural Networks in Networked Systems
by Rostyslav Olshevskyi, Zhongyuan Zhao, Kevin Chan, Gunjan Verma, Ananthram Swami, Santiago Segarra
First submitted to arxiv on: 8 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach for distributed online training of Graph Neural Networks (GNNs) is proposed to tackle the limitations of centralized training and distributed execution. This method, applicable to large-scale networked systems like wireless networks and power grids, efficiently trains GNNs while reducing development cycles. The technique adds only a few rounds of message passing to inference, with doubled message sizes for mini-batches. Empirical results in graph-based node regression, power allocation, and link scheduling demonstrate the effectiveness of this method under supervised, unsupervised, and reinforcement learning paradigms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GNNs are super smart tools that help computers learn from big networks like wireless networks and power grids. Right now, most GNNs need a central computer to train them, which makes it hard for them to adapt quickly. Researchers have found a way to make GNNs learn faster by training them on the network itself. This new approach is called distributed online training, and it works really well even when there’s not much data available. |
Keywords
» Artificial intelligence » Inference » Regression » Reinforcement learning » Supervised » Unsupervised
