# Trustworthy AI and Federated Learning for Intrusion Detection in 6G-Connected Smart Buildings
## 3 Related Work
### IDSs for Smart Buildings  
In the context of smart buildings, the deployment of Machine Learning-based Intrusion Detection Systems (ML-IDSs) is particularly advantageous due to the complex and dynamic environment inherent to these ecosystems. Traditional signature-based IDSs and rule-based approaches, while effective in certain scenarios, are often insufficient for the high volume of interactions and evolving threats characteristic of smart buildings. ML-IDSs, on the other hand, are capable of adapting to and learning from the system's behavior, providing a more robust solution that can detect novel and sophisticated threats without requiring constant updates [12].  
The implementation of ML-IDSs in smart buildings typically follows a centralized framework, which involves four key stages: data collection, pre-processing, learning, and interpretation [13]. Data are gathered from various sources, cleaned, and pre-processed to enhance their suitability for ML model training. The centralized approach allows for tailored pre-processing, which optimizes the learning process by ensuring that the model is attuned to the specific characteristics of the dataset. However, the model development and pre-processing tasks are often outsourced rather than conducted on-site within the smart buildings. This practice necessitates secure data transmission to external entities to prevent interception and tampering during transit.  
While this centralized approach facilitates effective ML model training, it introduces challenges related to data privacy, latency in model updates, and potential transmission bottlenecks. Consequently, it is essential to implement robust data management and security protocols to safeguard the integrity and confidentiality of data when deploying ML-based IDSs in smart buildings. These measures are critical to addressing the vulnerabilities that arise from the off-site development of ML models and ensuring the reliable operation of IDSs in these highly interconnected environments.  
By integrating the Intelligence of Everything (IoE) paradigm, the advent of 6G technology will significantly enhance the capabilities of ML-IDSs in smart buildings. This paradigm shift envisions the deployment of AI models at the edge of the network, improving efficiency, reducing latency, and improving data confidentiality [14]. To fully realize these benefits, it is crucial to automate data pre-processing and decentralize the learning process.  
In this context, FL emerges as a key enabler that allows ML models to be trained on data distributed across multiple devices and buildings while preserving data privacy [6; 15]. This decentralized approach marks a transition towards more secure and resilient ML-IDSs, addressing the challenges of data privacy and security arising in centralized models. Furthermore, the adoption of machine learning operations (MLOps) with a zero-touch architecture further automates the management of ML models, facilitating their seamless integration and continuous evolution within smart building security systems [16].