Federated Learning of Nonlinear Temporal Dynamics with Graph Attention-based Cross-Client Interpretability
The framework learns how different industrial subsystems interact over time without sharing raw data or modifying proprietary client models. It uses a central server with a Graph Attention Network to model these hidden cross-client dependencies through latent states. By linking the network's attention weights to the system's Jacobian, the method provides an interpretable way to understand temporal relationships.