Our paper “Distributed Scheduling using Graph Neural Networks” is accepted by IEEE ICASSP 2021

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In our latest paper “Distributed Scheduling using Graph Neural Networks” accepted by IEEE ICASSP 2021, we augment the distributed greedy scheduler with topology-aware node embeddings generated by Graph Convolutional Networks. Our approach can close the sub-optimality gap by half with minimal increase in the local communication complexity (as low as only one additional round of message passing). The deployment of GCN can be distributed while the training is centralized.

Preprint: https://arxiv.org/abs/2011.09430 Code: https://github.com/zhongyuanzhao/distgcn/