A model framework for the prediction of defects in strip steel is proposed with the objective of enhancing the accuracy of defect detection. Initially, the data are balanced through the utilisation of the Improved Synthetic Minority Oversampling Technique (ISmote), which is based on clustering techniques. Subsequently, further enhancements are made to the inertia weights and learning factors of the immune particle swarm optimisation (IPSO), with additional optimisations in speed updates and population diversity. These enhancements are designed to address the issue of premature convergence at the early stages of the process and local optima at the later stages. Finally, a prediction model is then constructed based on stacking, with its hyperparameters optimised through the improved immune particle swarm optimisation (IIPSO). The results of the experimental trials demonstrate that the IIPSO-ISmote-Stacking model framework exhibits superior prediction performance when compared to other models. The Macro_Precision, Macro_Recall, and Macro_F1 values for this framework are 93.3%, 93.6%, and 92.2%, respectively.

Federated learning, unlike centralized training, enables multiple clients to train a global model without exposing raw data, making it useful for applications such as medical diagnosis in smart healthcare and mobile edge networks. This setting is vulnerable to dynamic collusive attacks, where colluding clients coordinate abnormal parameter updates and adjust their strategies across training rounds. Existing studies address this problem with isolated mechanisms that focus on a single defense stage, but such mechanisms are insufficient for attacks that evolve across rounds. To address this challenge, we propose reputation-aware Stackelberg incentive aggregation (RSIA) to mitigate the influence of dynamic collusive attacks while balancing model utility in federated learning. Specifically, we first use incentive rules before local training to make repeated collusive behavior less beneficial. We then update client reputation after each round, so earlier unreliable behavior affects later incentives. Finally, we use reputation and update reliability to weight client updates, reducing the influence of abnormal updates on global model training. Experiments on three datasets show that RSIA reduces the average effective attack success rate by 84.9% compared with representative state-of-the-art (SOTA) methods. RSIA also maintains competitive test accuracy, providing a favorable trade-off between model utility and attack suppression.