Smart environments are increasingly equipped with interconnected digital systems to manage access and physical security. However, traditional authentication methods, typically restricted to static checkpoints, fail to provide persistent assurance once entry is granted, leaving facilities vulnerable to credential misuse, tailgating, and unauthorised movement. This paper presents the Continuous Authentication Platform (CAP), a modular, multi-modal framework developed within the RAAISE project to enable continuous and context-aware verification across dynamic facility zones. CAP integrates heterogeneous off-the-shelf sensors, including NFC, RFID, biometric, motion, and WiFi positioning units, which collectively support persistent user tracking and real-time access enforcement. The platform’s architecture couples distributed sensing and edge processing with a centralised intelligence layer for event correlation and policy-driven decision-making. A live testbed deployment at Deakin University was used to evaluate CAP’s performance under realistic operational conditions. Results from functional trials demonstrate CAP’s ability to detect credential misuse, prevent tailgating, and maintain authentication continuity with sub-second responsiveness. These findings underscore CAP’s potential as a scalable, privacy-aligned foundation for next-generation smart facility security systems.

Federated Learning (FL)-based Intrusion Detection Systems (IDSs) have recently surfaced as viable privacy-preserving solution to decentralized grid zones. However, lack of consideration of communication delays and straggler nodes in conventional synchronous FL hinders their applications within the real-world. To level the playing field, we propose a novel semi-asynchronous FL solution on basis of a preset-cut-off time and a buffer system to mitigate the adverse effects of communication latency and stragglers. Furthermore, we leverage the use of a Deep Auto-encoder model for effective cyberattack detection. Experimental evaluations of our proposed framework on industrial control datasets validate superior attack detection while decreasing the adverse effects of communication latency and straggler nodes. Lastly, we notice a 30% improvement in the computation time in the presence of communication latency/straggler nodes, thus validating the robustness of our proposed method.