## 4 Proposed Protocol
### Reputation Management
The below two figures depict the average reputation values of malicious vs. non-malicious voters at the end of each iteration, **(c)** starting with equal reputation values compared to **(d)** malicious voters having higher starting values. ### Requirement Comparisons Compared to Previous Works

Table 1 in Section 3 details the extent to which previous works have fulfilled the requirements. Table 4 shows the property comparison between the proposed scheme and other existing works. The benefits of MAM-FL are directly compared with two selected sources, namely References [7; 27]. These papers were chosen because they meet most of the requirements compared to the other references. **Confidentiality**: Reference [7] uses differential privacy to maintain the confidentiality of data in federated learning. Differential privacy has an advantage over encryption in that it allows for data sharing and analysis for scientific or social purposes, while protecting the privacy of individual data entries. However, this technique may introduce data errors or uncertainty and conflict with other privacy technologies. Reference [27] adds a standard model encryption in the FL on the participant keys. While encryption can prevent unauthorized access to data and preserve the exact values, it may limit data usage and analysis and expose the data to re-identification attacks. MAM-FL uses a signcryption scheme to encrypt the initial model, which combines the functionalities of digital signatures and encryptions in a single operation. This is a reasonable trade-off since signcryption provides other requirements that are not available in standard encryption, such as authentication and integrity. **Attractiveness**: References [7; 27] and MAM-FL use rewards to serve as motivation for participants to maintain good behavior and high performance, ensuring the quality and effectiveness of the federated learning process. **Accountability**: Both [27] and MAM-FL require participants to deposit a certain amount of tokens to participate, which creates an additional level of responsibility for the participants to continue the process until they receive their rewards. The risk of losing the stored tokens also adds protection against Sibyl attacks, hindering attackers who create multiple accounts. In Reference [27], a peer-review system was added, where each trainer acted as a reviewer for the other. However, this system can be unreliable since the trainers have to multitask and may lose focus. In contrast, MAM-FL assigns the roles of verifiers to voters, who contribute to the ranking of accepted model updates. This further helps establish accountability for each participant in the process without overwhelming them with too many tasks. **Reliability**: References [7; 27] and MAM-FL use blockchain, a distributed ledger, to record FL transactions. By nature, blockchain can address SPoF by allowing the participants to maintain a distributed ledger of the FL process, where each block records the model's updates and other relevant information. However, MAM-FL goes beyond simply integrating the process of each exchange between transmissions by utilizing smart contracts. Because storing model files can be costly, References [7; 27] and MAM-FL utilize decentralized storage, with IPFS being chosen in this case.