## 4 Proposed Protocol
### Reputation Management
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Reference** & **Confidentiality** & **Attractiveness** & **Accountability** & **Reliability** & **Consistency** & **Integrity** & **Authentication** \\ \hline \multirow{4}{*}{[7]} & Differential privacy & Deposit and reputation-based reward & Reputation system and IPFS & Blockchain and IPFS & - & - & - \\  & Standard encryption & Deposit and reputation-based reward & Reputation system and IPFS & Blockchain and IPFS & Stage timeout & - & - \\ \multirow{4}{*}{MAM-FL} & \multirow{4}{*}{Signcryption} & Deposit and reputation-based reward & Voting and reputation and IPFS & Blockchain based ranking & Accuracy-based fashion & Blockchain hash and signcryption \\ \cline{1-1} \cline{2-7}  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between works which requirements have fulfilled the most. **Consistency**: Reference [7] uses differential privacy, where the added noise can make it difficult to achieve convergence of the model across all clients. This is because the noise added to each client's data can be different, which can lead to inconsistencies in the model's updates. Reference [27] uses stage timeout to limit low-bandwidth trainers from submitting broken updates. In MAM-FL, a mechanism is included for tracking the reputation of each trainer based on their performance. Trainers with low reputations may be excluded from the process, ensuring that only high-performing trainers contribute to the model. This helps to protect the consistency of the scheme by weeding out low-performing trainers. **Integrity**: In References [7, 27], there is a lack of mention regarding integrity in FL, which can render the scheme vulnerable to attackers attempting to steal the model and perform modifications. In contrast, MAM-FL provides two methods to preserve integrity. First, to verify the signature of the encrypted model, Equation (1) requires a hash to be constructed, which will generate a different value if the model has been modified. Second, throughout the process, hashes of the model updates are verified to ensure the integrity of the model updates. The hashes are stored in the blockchain, while the model updates are stored in IPFS, which are then compared to guarantee that the contents are not modified. The added verification prevents data breaches since in the scenario of the stolen model, the contents can be compared to determine if the model is still genuine or not, preserving integrity. **Authentication**: In References [7, 27], the importance of authentication in FL is not mentioned. This introduces a vulnerability, where a malicious party could sabotage the aggregator by distributing fake versions of the model. The validity of the model cannot be determined. To solve such a problem, the MAM-FL uses signcryption to verify the legitimacy of the model, confirming whether the model is sent by the real owner or not. ## 6 Conclusions and Future Works

This research paper examines the requirements for establishing trust in a federated learning scheme, and identifies seven crucial factors, i.e., confidentiality, attractiveness, accountability, reliability, consistency, integrity, and authentication. The paper proposes MAM-FL, an FL scheme to address the requirements. First, the protocol integrates signcryption to encrypt the initial model to ensure the model's confidentiality, integrity, and origin authentication. Then, to enhance attractiveness, trainers and voters are guaranteed rewards based on their performance. Next, model updates are ranked and scored based on voter reputation, to address the accountability and consistency in the model updates. Lastly, the FL scheme is integrated with blockchain and IPFS to address single-point-of-failure, providing reliability. To evaluate the effectiveness of the proposed protocol, experiments were conducted to reduce the number of malicious actors and incentivize honest contributions through a reputation-based reward system.