## 5 Existing Solutions
### Other Solutions  
Some work to protect the IoT Blockchain-based federated learning adoption against poisoned attacks followed slightly different approaches. For instance, the work of [40] relied on the learning model accuracy to detect if there had been an attack attempt. The authors developed a mechanism comparing a new parameter update with known good model accuracy. If the parameter update degrades the model accuracy, the aggregator discards the update from aggregation. Their algorithm performed very well against 10%, 20%, and 30% of adversaries with very low accuracy degradation in the first round. However, as the number of rounds increases, the accuracy is not affected since malicious node updates are easily verifiable with good updates. Other works relied on time to prove one update's legitimacy. For instance, authors of [41] provided the block-FL framework to enhance data privacy while maintaining trustful collaborative learning with Blockchain and federated learning. Block-FL uses a decentralized hash table (DHT) to only store the hash of the data source on-chain while keeping the data off-chain. As usual, the federated learning process is an aggregation process used to update the global model after verifying local model updates by miners. For poisoning attack resilience analysis, they assumed that a malicious attacker would try to replace the global model with their poisoned model. To prevent such an attack, Block-FL follows a mechanism of evaluating the computation time proportionality with data size. Block-FL proved that the hash rate for an adversary to make turbulence on the ledger record is the lowest compared to federated learning and regular Blockchain. At the same time, the successful attack hash-rate requirement increases as the number of added blocks increases, which makes poisoning attacks even harder. A similar work by [42] recently provided a data-driven cognitive computing (D2C) framework based on Blockchain-enabled federated learning. The authors' main drive was to overcome the issues faced by industry 4.0 devices, such as privacy leakage. For the data, they chose to store the hashes only, instead of the whole data, using the DHT. For verification, the framework relied on consensus and intensive mechanisms using PoW and two types of incentives, token reward and data reward. The token reward was for miners, while the data reward was for end devices. The authors used proof of elapsed time (PoET) to spot poisonous model updates to verify the computation time with the data sample size. At each learning round, a temporary central server was chosen, an aggregator. Choosing an aggregator was based on the two-player game of the Markov decision process (MPC) between an aggregator and an adversary. Similar to [36], the hash-rate requirement increased as the number of blocks increased, making it harder for an adversary. However, there was still convergence latency in block generation time. Others used consensus mechanisms to protect data from leakage [42; 43]. For instance, authors of [42] applied federated learning on a permissioned Blockchain in an iIoT environment. In such a sensitive environment, data privacy and security matter the most. The authors proposed a scheme that follows federated learning aggregation procedures while verifying data provider nodes to prevent leakage. A data-sharing request goes through a proof of quality (PoQ) consensus mechanism to verify the data from each data provider node. The proposed scheme achieved good results with above 0.9 accuracies. However, increasing the number of data providers meant a degradation in both time and accuracy performance. Most federated learning with Blockchain integrations were with the iIoT infrastructure. Works of [44; 45; 46] primarily focused on applying privacy measures, designing an auditable record of transactions, and eliminating the central aggregation server of federated learning in their Blockchain integration. In [45], the authors designed a system that detects iIoT device failure.