  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.
