Cooperative Reinforced Resilient Federated Learning for Clients With Multiple Working Conditions

Collaborative modeling in industrial processes with similar production procedures and complex industrial parameters is an effective approach to address issues about online soft measurement. However, it is a challenging task for collaborative modeling at the presence of nonindependent and identically distributed (Non-IID) data, which is arising from variations in production materials and equipment. In this article, we propose a cooperative reinforced resilient federated learning for clients with multiple working conditions to solve the Non-IID problem by implementing a personalized strategy centered on local data storage, which can accurately build personalized models suitable for distributed data on a single client. Inspired by the success of multiagent reinforcement learning (MARL) in solving complex decision problems, we design a personalized aggregation mechanism guided by MARL and modeling process information. This mechanism optimizes model aggregation decisions based on data distribution and quality, thereby improving the effectiveness of the training process and enhancing the overall performance of personalized models. Meanwhile, we design an exploration and reward strategy for federated scenarios with distribution differences, which utilizes the collaborative modeling characteristics and advantages of federated learning to improve the exploration efficiency of reinforcement learning in solving personalized federated problems. Experimental results conducted on industry and benchmark datasets demonstrate that the proposed model surpasses existing methods in collaborative modeling under Non-IID scenarios.