# Privacy-Preserving and Explainable AI in Industrial Applications
## 2 Privacy Preservation in Industrial AI Applications
### Review of Privacy-Preserving AI in Industrial Applications  
In this section, we focus on research that targeted privacy-preserving artificial intelligence applied in industrial applications. Some of the main subjects in industry-oriented research are industrial Internet of things (IIOT) and Industry 4.0. A new method termed verifiable federated learning (VFL) was proposed by [PERSON] et al. [37] for privacy preservation in industrial IOT, which employs federated learning, while also allowing for information extraction from the shared gradients. Figure 2 illustrates the proposed federated learning framework. As mentioned above, a promising solution for privacy preservation is homomorphic encryption, but there are also other techniques for data encryption and/or anonymization. For example, on the basis of homomorphic data space transformation, [PERSON] et al. [38] proposed an anonymization algorithm to protect data, while still allowing neural network training. They analyzed the effects that this method has on the neural network perfor  
Figure 2: Federated learning framework proposed by [PERSON] et al. mance. By adding new frozen layers to the neural network, they succeeded in achieving anonymization, while the performance was slightly lower when compared to that of the original model. Blockchain can also be used instead of simple federated learning. [PERSON] et al. [39] employed blockchain to transfer models trained by customers, thus eliminating the need for federated learning for gradient updates. Because blockchain records are not altered, malicious manufacturers or customer activities are traceable. Another study highlighted the need for privacy preservation to ensure data protection when exchanging information between multiple owners of renewable energy power plants. The main goal of the data exchange is to increase the forecast performance. [PERSON] et al. [40] proposed a privacy-preserving framework that combines the alternating direction method of multipliers with data transformation techniques. Their method proved to be successful, being robust to privacy breaches and communication failures, while the forecast performance was only marginally lower than that obtained using a model without privacy protection. Generative adversarial network (GAN) is a machine learning algorithm that is widely used to generate synthetic data. Being first proposed in 2014 by [PERSON] et al. [41], GAN is currently popular and comes in different forms, one of them being least square generative adversarial network (LSGAN), which was used by [PERSON] et al. [42] together with federated learning to generate renewable scenarios. Through federated learning, a model was trained by gathering knowledge from different renewable sites, and then LSGAN was employed to generate renewable scenarios from the same distribution as the historical data, thanks to the capability of capturing the spatiotemporal characteristics of renewable powers. Below, we provide a concrete example of a privacy-preserving AI application for casting [43]: a manufacturing process in which a liquid material is usually poured into a mold, which contains a hollow cavity of the desired shape, and then allowed to solidify. Defects may appear during the casting process, e.g., blow holes, pinholes, burr, shrinkage defects, mold material defects, pouring metal defects, and metallurgical defects, which have to be detected, and the corresponding parts have to be removed. Typically, this process is performed by a human operator, who may not be 100% accurate and consistent in their decisions. A fully automated, AI-based approach may reach 100% accuracy and remove inter- and intra-user variability, i.e., improve the robustness of the detection. The manufacturer would typically decide to externalize the development of the AI model, which means that a large dataset containing photos of both acceptable and nonacceptable parts would have to be shared with the entity developing the AI model.