## 3 Requirements for Trustability in Federated Learning
All of this will cause the global model to be updated with broken chunks of updates, hampering accuracy. One proposed scheme [27] imposes a time limit to prevent low-connectivity trainers from sending broken or corrupt updates to the server. This solution attempts to prevent trainers with low connectivity from submitting their updates, which can harm the model's accuracy because the broken parts are being submitted instead of the whole parts. **R6 Integrity**  
Model integrity is critical in FL because the accuracy and effectiveness of the machine learning model depend on the quality and reliability of the data used for training. Any tampering or corruption of the data can lead to inaccurate or biased results, which can impact the performance of the model and its ability to make accurate predictions. In an FL environment, data are distributed across multiple devices or clients, and updates are made to the model by aggregating the results of the training performed on each device. As a result, it is essential to ensure that the data used for training are complete, accurate, and consistent across all devices. Blockchain technology can be used as an integrity solution for the federated learning training process by providing a transparent and tamper-proof ledger of all transactions and computations performed during the training process [20]. By using a blockchain, participants in the federated learning network can record their contributions to the training process, including the models they trained and the data they used. These contributions can be verified by the other participants in the network by using the blockchain, ensuring that all participants are contributing in a fair and transparent manner. **R7 Authentication**  
Authentication is critical in FL because it ensures that only authorized devices or clients participate in the training process and make updates to the machine learning model. Without authentication, there is a risk of unauthorized access to the data and the model, which can lead to data breaches, theft of intellectual property, and other security issues. In an FL environment, authentication is typically achieved through secure communication protocols, such as transport layer security (TLS) and secure sockets layer (SSL) [29]. These protocols encrypt the data being transmitted between devices or clients, ensuring that they cannot be intercepted or modified by unauthorized parties. Authentication can also be achieved through the use of digital certificates and public key cryptography. Each device or client can be issued a digital certificate that contains a public key, which is used to verify the identity of the device or client during the authentication process. The device or client can then use the private key to sign the updates it makes to the model, ensuring that they are authentic and cannot be tampered with. _Previous Approaches and Limitations_  
Several blockchain-based frameworks and protocols, such as BFL [5], BEAS [6], bytoChain [8], DeTrustFL [9], ModelChain [13], Twin FL [14], and SEC-CL [15] lack incentive mechanisms in their systems, which may cause participants to have no motivation to share their data or model updates with the model's owner, leading to low participation rates and poor learning performances. Meanwhile, DeepChain [12] offers blockchain-based FL with incentive- and deposit-based systems; however, it does not integrate a reputation system, which could serve as a vulnerability as attackers can masquerade as trainers. References [5; 16] utilized practical Byzantine fault tolerance (PBFT) as an alternative to PoW consensus during training. However, PBFT is limited in that the system cannot tolerate a number of malicious nodes in the network equal to or greater than one-third of the total number of nodes. While the reputation system can enhance the reliability ofthe system to some extent, there is a lack of external verification of the reputation, such as through a third-party review process.