Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling

• URL: http://arxiv.org/abs/2106.05223v1
• Date: Wed, 9 Jun 2021 17:12:43 GMT
• Title: Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling
• Authors: Chuizheng Meng, Sirisha Rambhatla, Yan Liu
• Abstract summary: We propose a graph neural network (GNN)-based architecture under the constraint of cross-node federated learning. CNFGNN operates by disentangling the temporal computation on devices and spatial dynamics on the server. Experiments show that CNFGNN achieves the best forecasting performance in both transductive and inductive learning settings.
• Score: 13.426382746638007
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vast amount of data generated from networks of sensors, wearables, and the Internet of Things (IoT) devices underscores the need for advanced modeling techniques that leverage the spatio-temporal structure of decentralized data due to the need for edge computation and licensing (data access) issues. While federated learning (FL) has emerged as a framework for model training without requiring direct data sharing and exchange, effectively modeling the complex spatio-temporal dependencies to improve forecasting capabilities still remains an open problem. On the other hand, state-of-the-art spatio-temporal forecasting models assume unfettered access to the data, neglecting constraints on data sharing. To bridge this gap, we propose a federated spatio-temporal model -- Cross-Node Federated Graph Neural Network (CNFGNN) -- which explicitly encodes the underlying graph structure using graph neural network (GNN)-based architecture under the constraint of cross-node federated learning, which requires that data in a network of nodes is generated locally on each node and remains decentralized. CNFGNN operates by disentangling the temporal dynamics modeling on devices and spatial dynamics on the server, utilizing alternating optimization to reduce the communication cost, facilitating computations on the edge devices. Experiments on the traffic flow forecasting task show that CNFGNN achieves the best forecasting performance in both transductive and inductive learning settings with no extra computation cost on edge devices, while incurring modest communication cost.

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