2026
Causality-inspired Federated Learning for Dynamic Spatio-Temporal Graphs
AAAI 2026technical
Federated Graph Learning (FGL) has emerged as a powerful paradigm for decentralized training of graph neural networks while preserving data privacy. However, existing FGL methods are predominantly designed for static graphs and rely on parameter averaging or distribution alignment, which implicitly