关键词: 时间序列预测, 安全隐私, 联邦学习, 个性化, 聚合, 酯化, 神经网络

Abstract:

In the production process of polyester fiber, accurate control of esterification temperature is crucial. However, due to the differences in production equipment and process parameters, traditional prediction methods are difficult to meet personalized needs, and there are privacy leakage and communication pressure problems in the data sharing process. The federated personalized adaptive time series forecast algorithm on esterification temperature is proposed, which protects privacy and reduces data communication pressure. Introducing a joint forecasting mechanism, the algorithm assigns private and shared models to each client, integrates model forecasting with learnable weights to enhance forecasting accuracy and generalization capability. An adaptive stage adjusts model parameters intelligently based on client data distribution and learns unique joint forecasting weights for each client to obtain personalized forecasting outputs. In addition, the adoption of Bayesian optimization algorithms over traditional methods like random search and grid search addresses issues related to high dimensionality, complexity, and resource constraints. This approach enables rapid identification of the optimal hyperparameter combinations for various algorithms. Experiments comparing the proposed algorithm with seven federated algorithms using real raw data collected from the distributed control systems of three domestic polyester fiber manufacturers show that the forecasting performance of the algorithm in this study excels across five different forecasting models. The proposed algorithm can provide support for newly established factories, reduce their production debugging costs, enhance production efficiency and product quality, and promote the intelligent development of the industry.

Key words: time series forecasting, security and privacy, federated learning, personalized, polymerization, esterification, neural networks