This research proposes a comprehensive collaborative intrusion detection system (CIDS) framework for IoT environments, integrating lightweight architecture, trust management, and privacy-preserving mechanisms. The framework employs a hierarchical architecture spanning edge, fog, and cloud layers to ensure efficient and scalable intrusion detection. Trustworthiness is enhanced through distributed ledger technology (DLT), leveraging blockchain frameworks to improve communication reliability among IoT devices. Federated learning (FL) is adopted to address privacy concerns, enabling collaborative data learning while preserving individual data privacy. The system is validated using the CICIoT2023 dataset, demonstrating high accuracy (97.65%), precision (97.65%), recall (100%), and F1-score (98.81%) in detecting various attacks on IoT systems. The proposed system is lightweight compared to traditional intrusion detection systems, with reduced network latency and memory consumption. The system combines lightweight architecture, trust management, and privacy-preserving techniques to create an adaptive CIDS for resource-constrained IoT environments. The research addresses the challenges of current CIDS methods, including scalability, trust, and privacy preservation. It introduces a multi-approach method to enhance CIDS effectiveness, utilizing edge-fog-cloud architecture, blockchain-based DLT, and FL. The system is validated using the CICIoT2023 dataset, which provides realistic IoT network traffic scenarios. The study contributes to the advancement of secure and resilient IoT infrastructures, addressing the need for lightweight, trust-managing, and privacy-preserving solutions in IoT environments. The proposed system demonstrates effective detection and mitigation of various security threats in complex IoT networks.This research proposes a comprehensive collaborative intrusion detection system (CIDS) framework for IoT environments, integrating lightweight architecture, trust management, and privacy-preserving mechanisms. The framework employs a hierarchical architecture spanning edge, fog, and cloud layers to ensure efficient and scalable intrusion detection. Trustworthiness is enhanced through distributed ledger technology (DLT), leveraging blockchain frameworks to improve communication reliability among IoT devices. Federated learning (FL) is adopted to address privacy concerns, enabling collaborative data learning while preserving individual data privacy. The system is validated using the CICIoT2023 dataset, demonstrating high accuracy (97.65%), precision (97.65%), recall (100%), and F1-score (98.81%) in detecting various attacks on IoT systems. The proposed system is lightweight compared to traditional intrusion detection systems, with reduced network latency and memory consumption. The system combines lightweight architecture, trust management, and privacy-preserving techniques to create an adaptive CIDS for resource-constrained IoT environments. The research addresses the challenges of current CIDS methods, including scalability, trust, and privacy preservation. It introduces a multi-approach method to enhance CIDS effectiveness, utilizing edge-fog-cloud architecture, blockchain-based DLT, and FL. The system is validated using the CICIoT2023 dataset, which provides realistic IoT network traffic scenarios. The study contributes to the advancement of secure and resilient IoT infrastructures, addressing the need for lightweight, trust-managing, and privacy-preserving solutions in IoT environments. The proposed system demonstrates effective detection and mitigation of various security threats in complex IoT networks.