In Industry 4.0 systems, a considerable number of resource-constrained Industrial Internet of Things (IIoT) devices engage in frequent data interactions due to the necessity for model training, which gives rise to concerns pertaining to security and privacy. In order to address these challenges, this paper considers a digital twin (DT) and blockchain-assisted federated learning (FL) scheme. To facilitate the FL process, we initially employ fog devices with abundant computational capabilities to generate DT for resource-constrained edge devices, thereby aiding them in local training. Subsequently, we formulate an FL delay minimization problem for FL, which considers both of model transmission time and synchronization time, also incorporates cooperative jamming to ensure secure synchronization of DT. To address this non-convex optimization problem, we propose a decomposition algorithm. In particular, we introduce upper limits on the local device training delay and the effects of aggregation jamming as auxiliary variables, thereby transforming the problem into a convex optimization problem that can be decomposed for independent solution. Finally, a blockchain verification mechanism is employed to guarantee the integrity of the model uploading throughout the FL process and the identities of the participants. The final global model is obtained from the verified local and global models within the blockchain through the application of deep learning techniques. The efficacy of our proposed cooperative interference-based FL process has been verified through numerical analysis, which demonstrates that the integrated DT blockchain-assisted FL scheme significantly outperforms the benchmark schemes in terms of execution time, block optimization, and accuracy.

翻译：在工业4.0系统中，大量资源受限的工业物联网（IIoT）设备因模型训练需求频繁进行数据交互，引发了安全与隐私方面的关切。为应对这些挑战，本文提出一种数字孪生（DT）与区块链辅助的联邦学习（FL）方案。为优化FL流程，我们首先利用具有丰富计算能力的雾设备为资源受限的边缘设备生成数字孪生，辅助其进行本地训练。随后，我们构建了一个FL延迟最小化问题，该问题综合考虑模型传输时间与同步时间，并引入协同干扰机制以确保数字孪生的安全同步。针对这一非凸优化问题，我们提出一种分解算法。具体而言，通过引入本地设备训练延迟上限和聚合干扰效应作为辅助变量，将原问题转化为可分解独立求解的凸优化问题。最后，采用区块链验证机制保障FL过程中模型上传的完整性及参与者身份可信度。通过应用深度学习技术，从区块链内经过验证的本地模型与全局模型中获取最终全局模型。基于数值分析验证了所提协同干扰FL流程的有效性，结果表明：集成数字孪生与区块链的辅助FL方案在执行时间、区块优化和准确率方面均显著优于基准方案。