This research introduces a comprehensive collaborative intrusion detection system (CIDS) framework designed to enhance the security of Internet of Things (IoT) environments. The proposed framework integrates lightweight architecture, trust management, and privacy-preserving mechanisms across edge, fog, and cloud layers. Trustworthiness is established through the use of distributed ledger technology (DLT) and blockchain frameworks, while privacy concerns are addressed using federated learning (FL). The system is validated using the CICIoT2023 dataset, demonstrating its effectiveness in enhancing the security posture of IoT ecosystems. Key contributions include a lightweight, scalable, and trust-managing CIDS that achieves high accuracy, precision, recall, and F1-score in detecting various attacks on IoT systems with heterogeneous devices and networks. The system's performance is superior to traditional centralized learning methods in terms of network latency and memory consumption, while maintaining robust trust and privacy.This research introduces a comprehensive collaborative intrusion detection system (CIDS) framework designed to enhance the security of Internet of Things (IoT) environments. The proposed framework integrates lightweight architecture, trust management, and privacy-preserving mechanisms across edge, fog, and cloud layers. Trustworthiness is established through the use of distributed ledger technology (DLT) and blockchain frameworks, while privacy concerns are addressed using federated learning (FL). The system is validated using the CICIoT2023 dataset, demonstrating its effectiveness in enhancing the security posture of IoT ecosystems. Key contributions include a lightweight, scalable, and trust-managing CIDS that achieves high accuracy, precision, recall, and F1-score in detecting various attacks on IoT systems with heterogeneous devices and networks. The system's performance is superior to traditional centralized learning methods in terms of network latency and memory consumption, while maintaining robust trust and privacy.