Self-Adapting Federated Continual Learning With Gradient Causal Constraint for Fault Propagation Path Identification

In modern industrial scenarios, federated learning (FL) has been widely adopted due to its advantages in privacy preservation and distributed modeling. However, existing FL approaches rely on a single update paradigm, which significantly hampers communication efficiency. Moreover, the server preserves historical aggregation states to model causal propagation across training rounds, instead of performing memoryless per-round aggregation. For this end, we propose a self-adapting federated continual learning with gradient causal constraint (GCC-SFCL). First, the selection of synchronous or asynchronous communication across FL rounds is reformulated as continual learning task, where an experience accumulation mechanism records historical aggregation states to model long-term temporal dependencies among client updates. Based on the aggregated context, we further design a self-adapting scheduling strategy driven by multidimensional client state information, enabling the server to dynamically select optimal aggregation. Furthermore, a causality-steering gradient constraint is introduced to explicitly regulate gradient updates, improving the interpretability of the causal model and allowing the capture of fault propagation paths. Experimental results of a simulation case and the real-world case demonstrate that GCC-SFCL significantly improves not only communication efficiency but also the accuracy of fault propagation path identification.