# Blockchain-Enabled Edge Intelligence for IoT: Background, Emerging Trends and Open Issues
## 4 Open Issues and Discussions
### Challenges and Research Gaps  
Although blockchain has many advantages according to our literature review, it is not perfect for edge intelligence in IoT. To realize the blockchain-enabled edge intelligence, there are still many research gaps in this area. We list some major challenges as follows:  
* _Selfish Learning_: In the Bitcoin system, selfish mining [120] may cause serious security and fairness issues. Selfish miners refer to a group of miners who collude to increase their reward. Minority groups or individuals could not compete with the selfish group because of their limited computing resources. This could further lead to the centralization of mining operations. Motivated by selfish mining, selfish learning attacks are attacking blockchain-enabled edge intelligence systems, where edge nodes exchange learning experiences and get the reward according to their contributions [121]. In such attacks, edge nodes collude in an FL scenario and accumulate model contributions. In this case, the selfish group will always win and get a reward. Moreover, other normal learning nodes tend to join in this selfish group for mining rewards. Furthermore, individuals can become selfish too. A single learning node may not powerful enough to win, so it just hides, waits, and accumulates its model contribution for future rewards. This could cause delays and decrease the quality of the global learning model.
* _Fork Issues_: Forks occur when the software of different mining nodes become misaligned. When edge nodes are not in agreement with the same learning model or algorithm, an alternative chain (i.e., a forked chain) emerges. Two potential conditions may cause a fork in a blockchain-enabled edge intelligence system. On one hand, the computing capability of MEC servers, which are close to the task publisher (i.e., the IoT device) geographically, is limited and relatively weak. A malicious attacker can deploy a powerful rogue MEC server close to the target task publisher. As therequirement of low-latency in edge intelligence is met with fast consensus mechanisms in blockchain systems, the mining puzzle could be very easily solved by this powerful rogue server. With malicious intentions in mind, this rogue node could start a fork to attack the global learning model. On the other hand, the global model could also fork accidentally if two learning nodes contribute the most and equally to the global learning model in the same iteration, as both model contributions are recorded at nearly the same time.
* _Transaction Rejection_: Edge nodes are resource-constrained, especially for IoT devices. Although there are several research works related to incentive mechanisms for IoT devices and edge servers, transaction rejection is still an unresolved problem. Most papers take the success of blockchain transactions for granted because miners are assumed to have a strong desire to record the transaction into a block for a reward. However, blockchain nodes could always refuse to participate in mining if the predefined reward is not good enough because solving computational puzzles costs a lot of energy. As resource-constrained devices, edge nodes may not spend their energy and join in the blockchain system because they have difficulty in recharging. In such cases, transactions are always rejected, and the consensus of a global learning model is hard to realize.