Anomaly detection is increasingly becoming crucial for maintaining the safety, reliability, and efficiency of industrial systems. Recently, with the advent of digital twins and data-driven decision-making, several statistical and machine-learning methods have been proposed. However, these methods face several challenges, such as dependence on only real sensor datasets, limited labeled data, high false alarm rates, and privacy concerns. To address these problems, we propose a suite of digital twin-integrated federated learning (DTFL) methods that enhance global model performance while preserving data privacy and communication efficiency. Specifically, we present five novel approaches: Digital Twin-Based Meta-Learning (DTML), Federated Parameter Fusion (FPF), Layer-wise Parameter Exchange (LPE), Cyclic Weight Adaptation (CWA), and Digital Twin Knowledge Distillation (DTKD). Each method introduces a unique mechanism to combine synthetic and real-world knowledge, balancing generalization with communication overhead. We conduct an extensive experiment using a publicly available cyber-physical anomaly detection dataset. For a target accuracy of 80%, CWA reaches the target in 33 rounds, FPF in 41 rounds, LPE in 48 rounds, and DTML in 87 rounds, whereas the standard FedAvg baseline and DTKD do not reach the target within 100 rounds. These results highlight substantial communication-efficiency gains (up to 62% fewer rounds than DTML and 31% fewer than LPE) and demonstrate that integrating DT knowledge into FL accelerates convergence to operationally meaningful accuracy thresholds for IIoT anomaly detection.

翻译：异常检测对于维持工业系统的安全性、可靠性与效率正变得日益关键。近年来，随着数字孪生与数据驱动决策的出现，多种统计与机器学习方法被提出。然而，这些方法面临若干挑战，例如仅依赖真实传感器数据集、标注数据有限、高误报率以及隐私问题。为解决这些问题，我们提出了一套数字孪生集成的联邦学习（DTFL）方法，在保护数据隐私和保持通信效率的同时提升全局模型性能。具体而言，我们提出了五种新颖方法：基于数字孪生的元学习（DTML）、联邦参数融合（FPF）、分层参数交换（LPE）、循环权重自适应（CWA）以及数字孪生知识蒸馏（DTKD）。每种方法都引入了一种独特的机制来结合合成知识与真实世界知识，在泛化能力与通信开销之间取得平衡。我们使用一个公开可用的网络物理异常检测数据集进行了广泛实验。以80%的目标准确率为基准，CWA在33轮达到目标，FPF在41轮，LPE在48轮，DTML在87轮，而标准的FedAvg基线方法与DTKD在100轮内均未达到目标。这些结果凸显了显著的通信效率提升（比DTML最多减少62%的轮数，比LPE减少31%），并证明将DT知识集成到FL中能加速收敛至对IIoT异常检测具有实际意义的准确率阈值。