Granular Neural Networks Learning for Time Series Prediction under a Federated Scenario

Abstract

Granular neural networks (GNNs) are a type of prediction models outputting information granules and GNNs not only provide more abstract results and a granular structure but also reveal a flexible nature that can be adjusted by users. As a promising tool, we apply GNNs to solve time series prediction problems under the federated learning (FL) scenario. Distributed time series prediction problems attract more attention recently due to the more usage of large quantity of IoT (Internet of Things) sensors and the development of Artificial Intelligence techniques. FL is the main approach to fix the distributed time series prediction problems. In this paper, we design a federated learning framework to refine granular weights of GNNs and then return better prediction results compared with the ones from centralized modeling. Different with the studies of FL using numeric neural networks, FL using GNNs is a study of aggregating parameters’ parameters under the federated scenario and thus the robustness and stability of the method is the most critical issue. To testify the two features of our method, we observe and compare from two aspects: different cases (several groups’ results) and different numbers of objectives (single-objective optimization and multiple-objective optimization). Experiments on predicting air quality index for 35 stations in Beijing (China) show the effectiveness of our method.