# Trustworthy AI and Federated Learning for Intrusion Detection in 6G-Connected Smart Buildings
## 3 Related Work
### Fl-Ids  
Several studies have explored the application of FL for intrusion detection. For instance, [PERSON] and al. proposed a federated deep learning model based on a recurrent neural network architecture for anomaly detection in smart buildings, demonstrating faster training convergence compared to centralized counterparts [30]. Similarly, other research efforts have focused on developing FL-based intrusion detection systems for various IoT environments [31; 32; 33]. These FL-IDSs allow individual devices (e.g., Unmanned Aerial Vehicles (UAVs) in a flying ad hoc network) to train a global intrusion detection model collaboratively without sharing raw, sensitive data [34]. This approach not only addresses privacy concerns inherent in centralized methods but also decreases communication costs, which is crucial in networks with frequent link breakages. [PERSON] et al. [35] proposed a proactive intrusion recognition method called FL-IDPP (Federated Learning Intrusion Detection with Privacy Preservation), which ensures privacy preservation for IoT networks. FL-IDPP employs bidirectional recurrent neural network (RNN) models to detect anomalies and identify potential intrusions. Data privacy and network efficiency are maintained by storing data locally on IoT devices and only sharing the learned model weights with the central server for FL. The method achieves high accuracy for the global ML model by incorporating a voting ensemble process to combine updates from multiple sources.  
In critical infrastructure, FL-IDSs often address specific data distribution scenarios, such as vertically partitioned data, where clients share different attributes about the same samples. Frameworks like FATE have been utilized to implement FL algorithms for intrusion detection in such scenarios [36].  
Research has also focused on enhancing the security and robustness of FL-IDSs. Defenses against poisoning attacks, which aim to compromise the global model by injecting malicious updates, are crucial for ensuring the trustworthiness of FL-IDS. Secure aggregation techniques can provide strong privacy guarantees but may introduce additional computational overhead and complexity. The integration of XAI techniques into FL-IDS designs allows for the investigation of AI reasoning, providing insights into the decision-making process of the models. Indeed, XAI enhances transparency for ML-based IDSs, especially when dealing with black-box IDS models [37].  
One approach combines FL with mimic learning to better preserve the privacy of end users, achieving performance to close to that of centralized deep learning while significantly improving privacy preservation [38]. Another enhancement involves a federated blending model (F-BIDS, Federated-Blending based Intrusion Detection System) to reduce reverse engineering attacks on the FL training process [39]. In F-BIDS, base classifiers are trained locally, and a meta-classifier is incorporated into the FL framework to further enhance data privacy.  
Moreover, lightweight FL solutions that minimize computational and energy overhead have become increasingly important, especially with the sustainability goals of 6G networks. The integration of zero-touch automation compatibility and the establishment of trusted AI frameworks are critical for securing AI solutions against current and future cybersecurity threats.  
While existing works have made significant contributions to the field, this paper builds upon existing research by proposing an FL-IDS to provide security and privacy in smart buildings that simultaneously considers both network data and sensor data. The proposed method incorporates FL-driven anomaly-based techniques, integrating zero-touch automation and establishing a trusted AI framework with XAI to enhance user confidence and ensure system integrity.