Federated Learning Based on Fuzzy Fusion Rules for Chemical Production Process Fault Diagnosis

Process data plays a vital role in diagnosing fault sources in chemical production. However, such data contain rich process information and are often sensitive, making direct analysis infeasible due to privacy concerns. Although federated learning mitigates data leakage risks, the conventional averaging strategy falls short in achieving high fault identification accuracy, especially under non-independent and identically distributed (non-IID) client data. To overcome this challenge, we propose a personalized federated learning framework, in which a Takagi-Sugeno (T-S) fuzzy fusion rule is designed. Then, the personalized model is constructed through a structured procedure: fuzzification of model parameter distances, definition of fuzzy rules, fuzzy inference, and defuzzification. Moreover, layer-wise fusion is employed to enhance the precision of aggregation. Evaluations on the Tennessee Eastman (TE) process demonstrate that our method achieves superior fault identification accuracy. The results validate the efficacy of the proposed Fuzzy Rule-Based Federated Layer-wise Fusion (FedFZ) framework in industrial fault diagnosis under heterogeneous data distributions.