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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., Comment: 28 Pages, 10 Figures

Machine Learning (ML) models combined with in-situ sensing offer a powerful solution to address defect detection challenges in Additive Manufacturing (AM), yet this integration raises critical data privacy concerns, such as data leakage and sensor data compromise, potentially exposing sensitive information about part design and material composition. Differential Privacy (DP), which adds mathematically controlled noise to ML models, provides a way to balance data utility with privacy by concealing identifiable traces from sensor data. However, introducing noise into ML models, especially black-box Artificial Intelligence (AI) models, complicates the prediction of how noise impacts model accuracy. This study presents the Differential Privacy-Hyperdimensional Computing (DP-HD) framework, which leverages Explainable AI (XAI) and the vector symbolic paradigm to quantify noise effects on accuracy. By defining a Signal-to-Noise Ratio (SNR) metric, DP-HD assesses the contribution of training data relative to DP noise, allowing selection of an optimal balance between accuracy and privacy. Experimental results using high-speed melt pool data for anomaly detection in AM demonstrate that DP-HD achieves superior operational efficiency, prediction accuracy, and privacy protection. For instance, with a privacy budget set at 1, DP-HD achieves 94.43% accuracy, outperforming state-of-the-art ML models. Furthermore, DP-HD maintains high accuracy under substantial noise additions to enhance privacy, unlike current models that experience significant accuracy declines under stringent privacy constraints., Comment: 28 pages, 13 figures