Abstract: Digital twin systems for Industrial Internet of Things (IIoT) operating under federated learning face dual challenges: catastrophic forgetting caused by continuously evolving data distributions and model knowledge erosion resulting from intermittent device offline behavior. To address these issues, this paper proposes a Knowledge-Persistent Federated Evolutionary Learning (KPFEL) framework that systematically mitigates knowledge forgetting through a coordinated "Storage-Constraint-Inheritance" mechanism. The framework comprises three core modules: (1) A knowledge persistence storage module that maintains independent storage units for each edge device on the server side, employing a momentum-based update strategy to preserve historical knowledge contributions from offline devices; (2) A knowledge-constrained aggregation module that treats historical gradient update directions as optimization constraints and efficiently computes global update trajectories compatible with historical knowledge via quadratic programming; (3) A generator knowledge inheritance module that synthesizes high-quality historical-class samples for data-free knowledge replay by integrating parameter inheritance, knowledge alignment, and adversarial training. Theoretical analysis proves that the framework achieves an convergence rate. Experiments on CIFAR-100, Tiny-ImageNet, and Stanford Cars datasets demonstrate that the proposed method yields an average improvement of 3.07 percentage points in classification accuracy and a reduction of 3.79 percentage points in forgetting rate over state-of-the-art baselines. Under extreme settings with only 20% client participation, the accuracy drop is limited to 5.21% compared to 15.84% for the baseline, exhibiting strong robustness against intermittent device offline behavior and providing an effective solution for privacy-constrained IIoT digital twin applications with continuously expanding categories.

摘要： 工业物联网数字孪生系统在联邦学习环境下面临双重挑战：数据分布持续演化引发的灾难性遗忘与设备间歇离线导致的模型知识流失。针对上述问题，本文提出知识持久化联邦演化学习框架，通过"存储—约束—传承"协同机制系统性缓解知识遗忘。该框架包含三大核心模块：（1）知识持久化存储模块在服务器端为各边缘设备维护独立存储单元，采用动量式更新策略保持离线设备的历史知识贡献；（2）知识约束聚合模块将历史梯度更新方向作为优化约束，通过二次规划高效求解与历史知识兼容的全局更新路径；（3）生成器知识传承模块融合参数继承与知识对齐策略，结合对抗训练机制合成高质量历史类别样本，实现无数据条件下的知识回放。理论分析证明该框架具有 的收敛速率。在CIFAR-100、Tiny-ImageNet和Stanford Cars数据集上的实验表明，所提方法较现有最优方法平均提升分类准确率3.07个百分点，降低遗忘率3.79个百分点；在仅20%设备参与的极端场景下准确率仅下降 5.21%（对比方法下降达15.84%），展现出对设备间歇离线的强鲁棒性，为隐私受限、类别持续扩展的工业物联网数字孪生应用提供了有效解决方案。