# Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues
## 3 Emerging Trends and Visions
### Decentralized ML in IoT  
* _Neural Networks_: Using neural networks in the Internet of medical things is a popular trend. However, medical data are privacy-sensitive and vulnerable to malicious attacks. [PERSON] et al. [92] proposed a federated approach for blockchain-based neural networks in Internet of medical things. It guaranteed distributed and local data storage for patients. * _Deep Reinforcement Learning_: This learning approach was widely used in academic research. However, most papers just applied deep reinforcement learning (DRL) mechanically for optimization purposes. Another trend is to explore its potential for IoT, especially in mobile blockchain applications. [PERSON] et al. [93] gave a task scheduling approach based on DRL to maximize the mining reward and minimize the cost. Moreover, a DRL approach was presented for blockchain-enabled MEC [94]. Cooperative task offloading was investigated in this paper. Furthermore, [PERSON] et al. [95] investigated routing control in blockchain-enabled MEC. A novel DRL-based approach was given for adaptive network routing. Moreover, [PERSON] et al. [96] investigated DRL and FL jointly. They proposed an intelligent ultra-dense edge computing framework and used DRL to make the offloading decision and to allocate resources. Furthermore, [PERSON] et al. [97] proposed a video analytics framework and DRL solutions to reduce the latency of the MEC system in the Internet of autonomous vehicles. Additionally, a framework was proposed for blockchain-enabled MEC. The adaptive resource allocation was given based on DRL approaches [98]. Furthermore, [PERSON] and [PERSON] [99] investigated deep Q-learning and Bayesian deep learning for the decision making in blockchain-based MEC. * _FL_: Learning in a federated way is not a new topic. However, blockchain-enabled FL emerges as a popular research trend. Its decentralized framework gives privacy and security in the learning process. [PERSON] et al. [100] proposed a blockchain-enabled FL for heavy-haul railways and implemented asynchronous collaborative learning in this federated system. Committee consensus was further devised for blockchain-enabled FL to reduce the cost of computing and increase security [101]. Moreover, cognitive computing has emerged as a new trend in Industry 4.0 networks [102]. A decentralized method was proposed for cognitive computing based on blockchain-enabled FL. Furthermore, [PERSON] et al. [103] investigated the unintended property leakage problem. They proposed a novel property inference attack to exploit this issue in FL. Plus, [PERSON] et al. [104] proposed a distributed and federated approach \begin{table}
\begin{tabular}{c c c} \hline \hline
**Directions** & **Ref.** & **Contributions** \\ \hline \multirow{4}{*}{Energy Trading} & [4] & Propose a novel knowledge and energy trading frame work based on permissioned blockchain. \\ \cline{2-3}  & [86] & Propose a peer-to-peer energy trading model based on the consortium blockchain. \\ \cline{2-3}  & [87] & Propose a novel power management platform based on the blockchain and FL for smart grid. \\ \hline \multirow{4}{*}{Entities Collaboration} & [88] & Design an incentive mechanism based on blockchain to enable collaboration in MEC-enabled video steaming. \\ \cline{2-3}  & [44] & Propose an incentive mechanism to award entities in FL crowdsourcing for smart home systems. \\ \cline{2-3}  & [89] & Develop a new proof of business consensus protocol to incentive entities in federated network slicing. \\ \cline{2-3}  & [90] & Propose a blockchain-enabled service composition solution. \\ \hline \multirow{2}{*}{Auction Mechanism} & [91] & Propose double auction mechanisms to motivate MEC servers in cross-server resource allocation. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Research directions and literature related to incentive mechanism in IoT. named as DFedForest, which was based on random forest algorithms and blockchain technologies. Additionally, a decentralized deep learning method called DDLPF was proposed for IoT applications [105].