Granular neural networks learning for time series prediction under a federated scenario

Granular neural networks (GNNs) are a type of advanced prediction models that produce information granules, offer more abstract and adaptable results. In this study, we address three significant issues in time series prediction within a federated learning (FL) scenario: the management of distributed data, the aggregation of GNNs, and the optimization of granularity levels. Traditional centralized models are insufficient for managing distributed data while ensuring privacy and reducing communication costs, and existing studies on GNNs have not explored their aggregation under a federated framework, which is essential for enhancing model robustness and stability. Additionally, determining the optimal level of granularity for GNNs remains a challenge, impacting the model's predictive accuracy and computational efficiency. To address these issues, we propose a novel federated learning framework that enhances the performance of GNNs for time series prediction. Our approach involves a comprehensive FL framework that enables the collaborative training of local GNNs, refining their granular weights through global aggregation, ensuring better privacy management, and reducing communication overhead. By focusing on the aggregation of parameters within the federated scenario, we enhance the robustness and stability of GNNs which are crucial for effective time series prediction. Furthermore, we determine the optimal levels of information granularity by employing multi-objective optimization techniques, specifically using Pareto fronts to balance the trade-offs between different objectives. Experiments on predicting air quality index for 35 stations in Beijing (China) show the effectiveness of our method. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.