Decentralized Graph Neural Network

Decentralized Graph Neural Networks (DGNNs) aim to leverage the power of GNNs for tasks requiring distributed computation and data privacy, avoiding the limitations of centralized approaches. Current research focuses on developing efficient algorithms for asynchronous communication and gradient aggregation across multiple nodes, often employing specialized GNN architectures like aggregated GNNs or tree-based GNNs to improve scalability and reduce communication overhead. These advancements are significant for applications ranging from collaborative robotics and privacy-preserving recommendations to efficient power grid management and large-scale traffic forecasting, enabling more robust and scalable solutions in distributed environments.