Federated Learning (FL) is essential for efficient data exchange in Internet of Things (IoT) environments, as it trains Machine Learning (ML) models locally and shares only model updates. However, FL is vulnerable to privacy threats like model inversion and membership inference attacks, which can expose sensitive training data. To address these privacy concerns, Differential Privacy (DP) mechanisms are often applied. Yet, adding DP noise to black-box ML models degrades performance, especially in dynamic IoT systems where continuous, lifelong FL learning accumulates excessive noise over time. To mitigate this issue, we introduce Federated HyperDimensional computing with Privacy-preserving (FedHDPrivacy), an eXplainable Artificial Intelligence (XAI) framework that combines the neuro-symbolic paradigm with DP. FedHDPrivacy carefully manages the balance between privacy and performance by theoretically tracking cumulative noise from previous rounds and adding only the necessary incremental noise to meet privacy requirements. In a real-world case study involving in-process monitoring of manufacturing machining operations, FedHDPrivacy demonstrates robust performance, outperforming standard FL frameworks-including Federated Averaging (FedAvg), Federated Stochastic Gradient Descent (FedSGD), Federated Proximal (FedProx), Federated Normalized Averaging (FedNova), and Federated Adam (FedAdam)-by up to 38%. FedHDPrivacy also shows potential for future enhancements, such as multimodal data fusion.

本研究解决了联邦学习在物联网环境中存在的隐私威胁问题，尤其是在动态系统中加入差分隐私噪声导致的性能下降。提出的FedHDPrivacy框架结合神经符号范式与差分隐私，智慧地平衡隐私与性能，在实际制造监控案例中表现出优于传统联邦学习框架的效果，提升幅度可达38%，显示出良好的应用潜力。

具有差分隐私的隐私保护联邦学习与高维计算