## 5 Existing Solutions
### Other Solutions
The second layer was the top chain (TC). The RSUs will also participated in the federated learning process by collecting data from their surrounding areas, merging their results with the transaction results from GC nodes, and uploading them to the chain. The PoK of [52] rewards higher accuracy workers with higher amounts, which will also work as an incentive mechanism. Dishonest workers are identified through a validation process of their achieved accuracy by the leader node. The proposed system achieved a result that was 10% higher compared with regular consortium Blockchain. Most of the found adoptions of federated learning with Blockchain did not address poisoning attack prevention. The authors of [53] suggested a model for market trading of resources in decentralized edge companies. In terms of privacy, they included federated learning that depends on requesting training models between companies instead of requesting raw data to prevent any possible leakage while fulfilling the requester's needs. Table 6 below summarizes the found literature by highlighting the integration scope and analyzing how they mitigate the previously mentioned privacy and security challenges. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Ref. **} & \multirow{2}{*}{**Consensus**} & \multirow{2}{*}{**Application**} & \multicolumn{6}{c}{**Integration Score1**} & \multicolumn{6}{c}{**Privacy and Security 2 ** & & & \multicolumn{3}{c}{**Consensus**} & \multicolumn{3}{c}{**Conf. **} & \multicolumn{3}{c}{**Incern. **} & \multicolumn{3}{c}{**Prov. **} & \multicolumn{3}{c}{**Irps**} & \multicolumn{1}{c}{**Pigol**} & \multicolumn{1}{c}{**P2**} & \multicolumn{1}{c}{**P3**} & \multicolumn{1}{c}{**S1**} & \multicolumn{1}{c}{**S2**} \\ \hline \multirow{6}{*}{Reputation-Based} & [24] & - & IoFT & \(\surd\) & - & \(\surd\) & - & \(\surd\) & - & - & \(\surd\) \\ \cline{2-13}  & [24] & BFTP & Mobile network & \(\surd\) & - & - & - & - & \(\surd\) & - & \(\surd\) \\ \cline{2-13}  & [24] & dBFTP & kU & \(\surd\) & - & - & - & \(\surd\) & \(\surd\) & \(\surd\) & - \\ \cline{2-13}  & [24] & - & iIoT & \(\surd\) & - & - & \(\surd\) & \(\surd\) & - & - & \(\surd\) \\ \cline{2-13}  & [24] & - & iEdge computing & - & \(\surd\) & \(\surd\) & \(\surd\) & - & \(\surd\) & - & - & \(\surd\) \\ \cline{2-13}  & [24] & BFTP & IoT infrastructure & \(\surd\) & \(\surd\) & - & \(\surd\) & \(\surd\) & - & - & \(\surd\) \\ \cline{2-13}  & [24] & - & iGoF computing & \(\surd\) & - & - & \(\surd\) & \(\surd\) & \(\surd\) & - & - & \(\surd\) \\ \hline \multirow{6}{*}{Noise-Based} & [24] & - & Mobile network & - & \(\surd\) & - & - & - & \(\surd\) & \(\surd\) & - & \(\surd\) \\ \cline{2-13}  & [24] & Algorand & mIoT & \(\surd\) & - & - & - & - & \(\surd\) & - & - & \(\surd\) \\ \cline{2-13}  & [24] & PoF & Edge computing & \(\surd\) & - & - & - & - & \(\surd\) & \(\surd\) & - & \(\surd\) \\ \cline{2-13}  & [24] & PoW & Edge computing & \(\surd\) & \(\surd\) & - & - & - & \(\surd\) & - & \(\surd\) \\ \hline \multirow{6}{*}{Other} & [24] & - & Edge computing & \(\surd\) & - & - & - & \(\surd\) & - & - & \(\surd\) \\ \cline{2-13}  & [24] & PoW & Fog computing & \(\surd\) & - & - & - & \(\surd\) & - & \(\surd\) & \(\surd\) \\ \cline{2-13}  & [24] & PoW & IoT & Industry network & \(\surd\) & \(\surd\) & - & - & \(\surd\) & \(\surd\) & - & \(\surd\) \\ \In this table, we compare relevant research works that used Blockchain and federated learning in the IoT ecosystem aiming to protect the privacy of client data. We arrange the table into six principles columns. The first column indicates the type of method which can be based on reputation, noise, or other. The second column indicates the reference itself. The third column refers to the applied consensus. The fourth column indicates the origin of data, which can be health data, from a mobile network, IoV data, iIoT data, edge data, or fog data.