## 5 Discussion and Future Challenges
### Importance and Challenges of Integrating Blockchain Technology and Deep Learning  
The emergence of blockchain allows deep learning applications to be applied in a wider range of fields because of the security and decentralization features provided by the blockchain. The blockchain can be considered a distributed, verified global database accessible by all nodes on the network. If machine learning models were built and deployed using blockchain technology, their development and deployment could be monitored in real time. When applied to deep learning algorithms, blockchain technology eliminates the effectiveness of data manipulation, model alterations, and other external attacks [137]. The main advantages of integrating deep learning and blockchain is summarized in Table 4.  
It is possible that consumers could be compensated for providing data required to train machine learning models thanks to the security mechanism provided by blockchain technology. Expert systems equipped with domain-specific knowledge bases make up the lion's share of current AI efforts. However, researchers are honing effective machine learning algorithms for application to real-world problems. When compared to deep learning, which places an emphasis on data usage to train models and provide accurate predictions, the blockchain stands out as a trustworthy, fault-tolerant technology. The immutability of the blockchain also protects deep learning models or data from cyber attacks and addresses concerns about data noise. In this study, we found that by integrating blockchain technology with deep learning models, accurate predictions can be achieved in a risk-free and trustworthy manner. Applications that are powered by deep learning can be used to collect, analyze, and use mission-critical data with the help of a secure, immutable, and distributed backbone provided by deep learning and blockchain.  
However, it should be noted that there are still many challenges associated with integrating blockchain and deep learning in current IoT systems. Without solving the fundamental defects of blockchain, e.g., transaction efficiency, block size, power consump  
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Merits** & **Type of Blockchain** & **Description** \\ \hline Improved robustness & Private/public & The combination of blockchain technology and deep learning can be useful in business settings, where parties can work together in a trustless and automated manner. \\ \hline Automatic decision making & Private & With the help of the decision traceability feature of deep learning models, verifying that choice is a breeze. Furthermore, it ensures that the documents were not tampered with during the auditing process with human involvement. \\ \hline Joint decisions & Private/public & By employing swarm robotics to blockchain technology, robots can obtain access to a voting-based approach that can help them make an informed decision based on the data they have collected. \\ \hline Information assurance & Private & When fed consistent data from the blockchain, deep learning algorithms can make better, more informed decisions. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Advantages of deep learning and blockchain integration.  
tion, etc., many deep learning solutions are still in their nascent stages, representing only proof of concept [138]. One of the fundamental concerns is that IoT devices might be overwhelmed by running both blockchain and deep learning, both of which intensively consume computation resources. Furthermore, most IoT applications require real-time application, which exacerbates the case of applying both blockchain and deep learning. Privacy adds another level of difficulty regarding both the data and deep learning models in the IoT.