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 continua