# Trustworthy AI and Federated Learning for Intrusion Detection in 6G-Connected Smart Buildings
## 1 Introduction  
Smart buildings are a significant step toward creating more efficient, sustainable, and user-friendly living and working environments. The term \"smart building\" is distinct from \"automated building\" due to its emphasis on the \"intelligence quotient\" [1]. A reference model for analyzing these systems is presented in [2], depicting a building equipped with sensors, actuators, smart devices, control entities, and user interfaces. The smart building ecosystem integrates IoT devices, local or cloud-based control systems, and user interfaces for manual control and status monitoring. These advanced ecosystems interconnect every device, from lighting to Heating, Ventilation, and Air Conditioning (HVAC) systems, creating a seamless and intuitive experience for users. This integration provides high levels of comfort and energy efficiency, indicating a future where buildings adapt to our needs and anticipate them. The integration of diverse technologies originally designed for specialized environments--such as industrial control and medical applications--into aconsumer landscape introduces new risks and vulnerabilities. Attack vectors emerge from communication media, control software, and supply chains. Communication protocols like Wi-Fi, ZigBee, Z-Wave, and legacy systems such as BACnet and Modbus are vulnerable to attacks, including Man in The Middle (MITM), replay, and SYN flooding, due to weak encryption and authentication [3; 4]. Control software, reliant on third-party apps, as well as operating systems, firmware, and automation tools, faces threats like privilege escalation, Remote Code Execution (RCE), and firmware tampering [4]. Device-dependent vulnerabilities, such as sensory channel manipulation and supply chain risks, further exacerbate security challenges [3]. Attacks can have both cyber impact (e.g., breaches of confidentiality, integrity, and availability) and physical impact (e.g., unauthorized actuation, denial of service, and privacy violations), highlighting the need for robust, tailored security measures [3].  
To address these challenges, we introduced a preliminary analysis of a centralized multi-layer Intrusion Detection System (IDS) for smart buildings in [5]. Building on this work, this paper proposes a novel approach by designing and developing a distributed, privacy-preserving artificial intelligence (AI) system for intrusion detection. The solution is driven by Federated Learning (FL), a state-of-the-art approach for training accurate ML models using data from different sources without sharing them. This allows for the preservation of the confidentiality of user information, which is extremely critical in smart building scenarios where personal and sensitive data are involved. Unlike traditional AI models that rely on centralized data processing, FL decentralizes the learning process, allowing data to remain on local systems. This feature not only significantly mitigates the risks associated with data transmission and storage but also ensures privacy by design. This approach meets the growing demand for privacy-aware technologies by providing a way to bridge the benefits of smart buildings with the need to protect personal privacy. This paper also looks ahead to the upcoming evolution of network technologies--in particular, the transition to 6 th Generation (6G) networks. With the advent of 6G, AI solutions will be increasingly deployed at the edge of networks, further decentralizing data processing and decision making. This shift brings new requirements for AI systems, including minimal overhead, sustainability, and the implementation of zero-touch operations for Machine Learning Operations (MLOps).