## 5 Existing Solutions
### Reputation-Based  
Researchers in this area focused on finding a way to vouch for a learner node's legitimacy. One integration is in the work of [29], where the authors proposed a system of federated learning that analyzes people's readings of the late COVID-19 virus to classify and detect infected persons. They relied on Blockchain to use smart contracts and consensus mechanisms to calculate the scores and reputation for each participating edge device. In addition, the system uses Blockchain and smart contracts as provenance providers to limit the access to storage records. The proposed federated learning scheme achieved an accuracy of 90% in training and 85% in testing. Performance-wise, the edge devices have high and moderate energy consumption levels. This is because of the live model gradients sending and applying heavy encryption algorithms. Similarly, the work of [30] has also relied on the worker, or learner, node reputation. Researchers proposed a reputation technique that identifies a malicious worker. This technique aggregates reputation from the other workers to decide that worker's legitimacy. A malicious worker would train wrongful data to propagate false updates to the global model, where these updates would affect that model's performance. However, they follow a framework of reputation that allows the task publisher to select a worker based on their reputation. Their experiment was with a 10-node network, which included two malicious workers. The lowest training accuracy was 76.12%, with two malicious workers and an attack strength of 0.9 out of 1.0. Their proposed framework achieved slightly better results than other trust-reputation-based frameworks. Moreover, the work of [31] implemented federated learning into Blockchain to collaboratively train a traffic flow prediction (TFP) system of a neural network (NN) learning model. The authors designed a system to preserve vehicle update privacy and protect against poison attacks. This system relied on a consensus algorithm based on a delegated Byzantine fault tolerance (dBFT) protocol to determine low-quality or negative model updates. The proposed system performed well in poisoning attack prevention. The attack success rate (ASR) stayed under 10% with integrated federated learning compared to over 25% in unintegrated federated learning with 10%, 20%, and 30% malicious vehicles. Authors of [32] proposed yet another reputation-based framework called TrustFed. The authors designed TrustFed to allow IoT devices to collaboratively train a global model based on cross-device federated learning (CDFL) schemes and Blockchain. The federated learning training was performed off-chain; data storage was also off-chain through Inter-Planetary File System (IPFS). More importantly, TrustFed's smart contract integration used three smart contracts: incentive, aggregator, and reputation. The incentive is to reward miners who contribute to validating the model updates. The aggregator is to choose the central server at each learning round. Lastly, the reputation smart contract measures the reputation of participating nodes, thus avoiding malicious device participation. TrustFed relied on off-chain and on-chain processes to determine the reputation scores of each participating node. Off-chain procedures include statistical analysis to detect outliers' updates at each learning round. On-chain procedures include aggregating trust scores where the device trust score is either incremented or decremented by 100 based on the latest fair training performance. Experimenting on actual sensors' reading data showed that outlier detection (with worker nodes' reputation) achieved much better learning results. Malicious workers' updates were defined and removed from the next iteration, so aggregator loss was lower each time. Adding more workers to the learning task meant more transactions, creating communication overhead. However, the authors claim it was much less than transmitting raw data.