Abstract In recent years, cloud-edge-end collaborative federated learning frameworks have been widely used in many scenarios and achieved good results. However, with the complexity of application requirements, the problems of device heterogeneity and data heterogeneity become more prominent. Traditional frameworks often face challenges such as uneven allocation of computational resources and inefficient training when dealing with these problems. To address this problem, this paper proposes a novel federated learning framework for multi-domain collaborative analysis of networked encrypted flows. First, we split the model training tasks, intelligently assign part of the model training tasks to terminal devices based on their performance, while the remaining model training tasks that require more computational resources are handed over to edge servers. Second, we introduce a resource scheduling scheme among edge servers to reasonably allocate model training tasks and fully utilize resources. Finally, high quality global models are obtained through a weight-enabled global model aggregation scheme. Experiments show that our proposed scheme can effectively address the impact of device heterogeneity and data heterogeneity in encrypted traffic identification in cross-domain networks, and improve the training efficiency and model performance of the overall system while ensuring data privacy and security.
He et al. (Thu,) studied this question.