## 5 Existing Solutions
### Noise-Based  
Instead of calculating trust scores based on the worker node's contribution, other works added well-defined noise on the model updates to detect malicious model updates. For instance, authors of [36] proposed a framework to train an ML model using Blockchain-based federated learning in 5G networks. The framework divides the process into three main phases: initialization, aggregation, and updating. A task publisher node initiates a training task by sending the testing dataset, initial model, evaluation criteria (accuracy), and reward (monetary). Aggregation includes nodes training ML models locally, evaluating them to meet training criteria, and adding well-defined noise to protect the ML gradient's privacy. The updating includes the central server evaluating the updates and aggregating them to update the global model and send it to the publisher. A node is rewarded when it achieves the accuracy threshold or higher. During experiments, the more the threshold for privacy increases, the more the accuracy decreases. However, adding more participants (\(p=300\)) achieved higher accuracy results. In [37], the authors illustrated a use-case of a smart healthcare system that uses federated learning and Blockchain in Medical IoT (mIoT) devices. In their paper, they addressed the privacy of patients' records by using an adaptive DF technique that adds noise to training gradients. To defend against poisoning attacks, the authors proposed a consensus protocol that identifies poisoned gradients through a verification committee. The proposed mechanism acknowledged a slight performance accuracy loss despite preserving higher privacy in the results. From a performance perspective, the proposed system is slower than regular federated learning since it applies consensus algorithms. Their proposed system of detecting poison attacks performed well, keeping the attack success rate lower than 20% compared to over 50% in the regular FL. Moreover, authors of [38] proposed Biscotti, a framework for Blockchain-based federated learning. The choice of peers at the learning round in Biscotti relied on the proof of federation (PoF) consensus algorithm to coordinate collaborative learning among the peers. Similar to the works of [35], to prevent poisoning attacks, they used a Byzantine tolerance aggregation scheme called Multi-Krum, which validates the peer model update by comparing it with other peers and measuring the noise difference. Noisier nodes are responsible for adding noise to model updates to prevent privacy leakages and help detect poisonous attacks. With 30% of malicious nodes, the Biscotti test error was low compared to regular federated learning. At training, and with 200 nodes, as the time increased, the training error rates were kept low. However, if the malicious nodes were 50% of the participating nodes or more, the attacker could easily alter the global model outcome. Like Biscotti, The authors designed BAFL [39] as a novel Blockchain-based asynchronous federated learning framework. BAFL controlled the updates of the global and local models of devices by identifying entropy noise values. The design of BAFL was to overcome regular synchronous federated learning, such as FLAvg, which usually could cause significant performance delays. BAFL has two main layers, the device layers (D) and the miners' layer (M). At each learning epoch, a miner randomly connects to a device. A device loads the global model and locally trains it. The device uploads the training parameters, including the time duration of one training round. The miner verifies the device update with the global model before adding it to the Blockchain. To detect poisoning attacks that could tamper with the model's parameters, BAFL used entropy noise to measure the authenticity of each device. BAFL has achieved lower resource consumption and delay during experiments than the original synchronous AvgFL framework. In addition, BAFL also improved learning precision by 12.1%.