A robust federated biased learning algorithm for time series forecasting

Abstract

The federated averaging algorithm (FedAvg) is extensively used for multi-sensor data modeling but often overlooks the unique characteristics of local models when privacy and data security are not considered. This study introduces a novel federated learning algorithm built upon the FedAvg framework, which emphasizes the specificity of each local model to optimize global knowledge aggregation. The algorithm's effectiveness is demonstrated through an air quality index prediction problem, showcasing superior prediction performance and robustness in noisy data scenarios. Additionally, the study delves into the reliability and robustness of the proposed approach, addressing the prevalent notion that centralized learning methods often surpass federated learning when data security is not a concern. Our experiments affirm the necessity and superiority of federated learning methods, even in the absence of privacy considerations, by effectively managing real-world noisy data.