## 2 Related Work  
The trend of integrating federated learning and Blockchain is still fairly new. Consequently, there are not many works in the literature that summarize the combination of both technologies in IoT from a security perspective. The authors of [10] presented a comprehensive survey that investigated the security and privacy concerns of Blockchain and FL (BCFL) integrations. They studied the functions of BCFL elements including verification mechanisms, model aggregation, and incentive mechanisms. In addition, the authors analyzed current BCFL security and privacy challenges. The authors of [11] presented a systematic survey that reviewed the Blockchain-based federated learning approaches from a security and privacy perspective. The authors presented state-of-the-art results of combining federated learning and Blockchain while studying relevant security and privacy concerns. Although the work of [10; 11] were thorough, they did not review works from an IoT perspective. However, the authors of [12] presented a survey of Blockchain and federated learning integration in IoT. They provided a review of the Blockchain, federated learning, and IoT taxonomy while considering basic security and privacy concerns. In [13], the authors proposed a comprehensive survey discussing the use of FL techniques to secure IoT-based systems. They also outlined existing solutions and future trends related to IoT data. In their paper, the authors discussed security and privacy issues in the IoT ecosystem. The research works in [14; 15; 16] discussed the use of federated learning and Blockchain in the Internet of Vehicles (IoV). The authors outlined existing solutions that deal with applying FL and Blockchain for security and privacy-preserved methods in the IoV ecosystem. In [14], the authors proposed a solution that used homomorphic encryption in addition to FL and Blockchain. Their aim was to improve the privacy preservation of user data. In [15], the authors presented a comprehensive survey that aimed to discuss existing solutions in the field of IoV. In addition, they presented the challenges and future trends for methods aimed at dealing with the privacy issues in the IoV. Table 2 summarizes the discussed work with their limitations. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Related Work** & **Summary** & **Limitation** \\ \hline
[10] & Studied the security and privacy issues of Blockchain and federated learning integration. & Did not cover integrations related to IoT. \\  & & Furthermore, no consideration on poisoning \\  & & & attack mitigations. \\ \hline
[11] & Explained federated learning approaches with concern to privacy and security issues. & The wide scope of the paper did not focus on IoT-related integrations, with no mention of poisoning attack mitigations. \\ \hline
[12] & Presented a survey that studied the integration between Blockchain, federated learning, and IoT with studying. & Did not provide analysis on the found literature and how the poisoning attacks are mitigated. \\ \hline
[13] & Propose a comprehensive survey discussing the use of FL techniques to secure IoT-based systems. & Did not outline adversarial machine learning attacks and how to tackle them. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of related work. In [9], the authors discussed the Blockchain trends in a general way and did not give high priority to privacy-preservation issues. The work of [10] lacked integrations related to IoT. In addition, the authors did not discuss the poisoning attacks that could happen in the learning phases. The main drawbacks of [11] are also discussing privacy and security issues based on federated learning, but this work did not consider the IoT specificities. Finally, in [12], the authors did not debate how the poisoning attacks are mitigated. Although it is close to this paper's scope, the work of [12] did not review existing solutions that mitigate the poisoning attack threat.