# An Overview of Privacy Dimensions on the Industrial Internet of Things (IIoT)
## 3 IIoT and Privacy-Preserving Architectures
Figure 3 presents the Federated Auto-Meta-Ensemble Learning (FAMEL) architecture in the new IT/OT industrial environment [45]. It is a holistic system that automates selects and uses the most appropriate algorithmic hyperparameters to optimally solve a problem under consideration, approaching it as a model for finding algorithmic solutions where it is solved via mapping between the input and output data. The proposed framework uses meta-learning to identify similar knowledge accumulated in the past to speed up the process. This knowledge is combined using heuristic techniques, implementing a single, constantly updated intelligent framework. The data remains in the local environment of the operators, and only the parameters of the models are exchanged through secure processes, thus making it harder for potential adversaries to intervene with the system. Here are a few examples of how federated learning technology has been applied in different scenarios to enhance privacy:  
1. Healthcare [46]: Federated learning can be applied in healthcare settings to enable collaborative model training while preserving patient privacy. Hospitals or medical institutions can train machine learning models using local patient data without sharing sensitive patient information. The models are then aggregated or updated in a privacy-preserving manner, allowing healthcare providers to benefit from shared insights without compromising patient confidentiality. This approach can be useful for applications such as disease diagnosis, medical image analysis, or predictive analytics. 2. Smart Devices and the Internet of Things (IoT) [47]: Federated learning is well-suited for scenarios involving edge devices or IoT devices. These devices often have limited computational resources, making it challenging to send large amounts of data to a centralized server for model training. With federated learning, edge devices can collaboratively train machine learning models using locally collected data while keeping the data on the device. Only model updates or aggregated information is sent to a central server, ensuring privacy while benefiting from shared knowledge. This is useful in applications such as personalized recommendations, activity recognition, or anomaly detection in smart homes or industrial IoT settings. 3. Financial Services [48]: Federated learning can enhance privacy in financial services by enabling collaborative model training while keeping sensitive customer data on local servers or devices. Banks or financial institutions can train machine learning models for tasks like fraud detection or credit scoring using locally held customer data. The models' updates or aggregated information are exchanged in a privacy  
Figure 3: Basic security reference architecture in the industrial environment [45]. preserving manner, ensuring the privacy of individual customer transactions and sensitive financial information. 4. Natural Language Processing (NLP) [49]: Federated learning can be applied in NLP tasks to protect user privacy while improving language models. Instead of centralizing user data on a single server, federated learning allows individual devices or servers to train language models using local data. The models' updates or aggregated information, which preserve the privacy of individual texts, are shared across devices or servers. This approach enhances privacy while enabling the improvement of language models for applications such as voice assistants, chatbots, or sentiment analysis. These examples illustrate how federated learning can be leveraged in various domains to enable collaborative model training while maintaining privacy and data confidentiality. By keeping the data decentralized and only exchanging model updates or aggregated information, federated learning offers a privacy-enhancing approach for machine learning in scenarios where data privacy is crucial. A discussion and comparison of these two approaches (Blockchain and Federated Learning) are presented in the Table 2.