An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

Zhihao Hao, Guancheng Wang, Bob Zhang, Leyuan Fang, Haisheng Li

Abstract: Dear Editor, This letter deals with a solution for time-varying problems using an intelligent computational (IC) algorithm driven by a novel decentralized machine learning approach called isomerism learning. In order to meet the challenges of the model's privacy and security brought by traditional centralized learning models, a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination, thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process. Moreover, nodes in the network are equipped with different models to meet many challenges caused by the model silos. Furthermore, an integration scheme is introduced to efficiently obtain the global solutions of time-varying problems. In this letter, the convergence of the proposed model is theoretically proven, where its efficiency is validated via experiments, which shows that it outperforms many state-of-the-art models using centralized processing.