## 2 Related Works  
This research explores the pressing challenges of enhancing security measures in the IoT landscape, which has seen exponential growth and increasing vulnerability to cyber attacks and exploitation. A notable concern is the proliferation of various and distributed attacks, orchestrated by compromised IoT systems, which pose a substantial threat to servers and network services. CIDSs have played a pivotal role in defending against these threats, employing both signature-based and anomaly-based detection methods. Nevertheless, these conventional CIDS solutions have demonstrated limitations that require novel approaches when dealing with various and distributed attacks to detect them quickly and securely. The study conducted by [11] highlights the significance of securing IoT ecosystems due to their increasing interconnectivity and the sensitive data they manage. It introduces a hierarchical blockchain-based FL (HBFL) framework, stressing the importance of cyber threat intelligence (CTI) sharing among organizations utilizing ML-based IDS in IoT environments. This framework operates in an FL setting, utilizing blockchain-based smart contracts to promote trust and cooperation among organizations. It places a strong emphasis on safeguarding data privacy and demonstrates the practical feasibility of the solution through implementation and assessment using two prevalent IoT datasets, namely, NF-UNSW-NB15-v2 and NF-BoT-IoT-v2. The study conducted by [12] introduces FIDChain IDS, a federated intrusion detection system customized for blockchain-enabled IoT healthcare applications, utilizing lightweight artificial neural networks (ANNs) within an FL framework. By leveraging machine learning, edge computing, and blockchain technologies, the system provides robust security measures to protect the privacy of medical data. The research demonstrates the superiority of ANN models over eXtreme Gradient Boosting (XGBoost) models in managing diverse IoT device data, especially in healthcare environments, using the BoT-IoT dataset. Through experimentation with various datasets, such as CSE-CIC-IDS2018, Bot Net IoT, and KDD Cup 99, it is observed that the BoT-IoT dataset produces consistent and precise outcomes for assessing IoT applications in healthcare. Additionally, the paper introduces a solution based on private and permissioned blockchain for the FL process, ensuring efficient and secure data sharing and aggregation compared to public blockchains that may exhibit inefficiencies in weight updates. Research by [13] introduces a collaborative intrusion detection algorithm based on a CGAN (conditional generative adversarial network) with blockchain-enabled distributed FL. The algorithm utilizes FL to allow multiple MEC (mobile edge computing) nodes to collectively train a global intrusion detection model while ensuring decentralized and secure data management. The CGAN framework consists of a generator and a discriminator, where the generator creates synthetic intrusion samples and the discriminator distinguishes between real and synthetic samples. Moreover, blockchain technology is integrated into the algorithm to maintain the integrity and transparency of the training process. The validation of the study is conducted using the CICIDS2017 dataset. The study by [14] presents a federated deep learning-based intrusion detection framework (FED-IDS) tailored for smart transportation systems, which delegates the learning process to distributed vehicular edge nodes. A context-aware transformer network is integrated into the framework to analyze spatial-temporal patterns of vehicular traffic flows to detect various types of attacks. The methodology employs blockchain-managed federated training to support secure, distributed, and reliable training across multiple edge nodes without a central authority. Miners in the blockchain validate decentralized local updates from participating vehicles to prevent the inclusion of unreliable information in the blockchain.