## 3 Requirements for Trustability in Federated Learning
Reference [7] proposed a blockchain-based FL framework with differential privacy as encryption. Differential privacy helps protect the privacy of individuals in a dataset by adding random noise to the data or the queries. This noise introduces a trade-off; while providing privacy, it can affect the utility or accuracy of the data analysis [30]. Reference [10] proposed a permissioned blockchain-based federated learning method, where incremental updates to an anomaly detection machine learning model are chained together on the distributed ledger. However, this paper focused more on the machine learning model and did not mention much about the importance of incentive mechanisms and other aspects, such as security, accountability, or confidentiality of FL. Reference [11] leveraged blockchain-sharding features to enable collaborative model training in a more distributed and trustworthy manner. Blockchain sharding, however, can compromise security and decentralization, as each shard may have fewer nodes and less hash power than the whole network, making it more vulnerable to attacks or collusion. Reference [27] proposed a blockchain-based scheme with a leveling system, wherein trainers gain experience and reputation over time, leading to better rewards for their performance. However, since the proposed peer-review system forces each trainer to also act as a reviewer, it can be deemed unreliable due to the necessity of multitasking [31]. The research conducted by [32] centered on the integration of FL in a vehicular ad hoc network (VANET) environment. The study assessed the performance of devices, the efficacy of ML and aggregation algorithms, the effects on edge-to-server communication, and resource consumption. However, it did not address the incentive mechanism, confidentiality, and reliability aspects essential for sustaining federated learning. Reference [33] introduced the incorporation of local differential privacy and zero-knowledge proof in a blockchain-based FL framework. While this work provides confidentiality using differential privacy and incentives, it does not provide a trust management system to combat malicious actors masquerading as honest workers. Based on these observations, Table 1 shows which references satisfied the requirements listed in Section 3, marked by, while \(\times\) is for the unsatisfied ones. To improve accountability, MAM-FL utilizes a voting-based system to select models for averaging. In the beginning, trainers must first deposit a specific amount of tokens in  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Confidentiality** & **Attractiveness** & **Accountability** & **Reliability** & **Consistency** & **Integrity** & **Authentication** \\ \hline
[5] & \(\times\) & \(\times\) & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) \\
[6] & ✓ & \(\times\) & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\
[7] & ✓ & ✓ & ✓ & ✓ & \(\times\) & \(\times\) & \(\times  
order to join the network and participate in the training and voting process. This deposit system serves as an added layer of security, as it ensures that clients have a vested interest in the network and are less likely to act maliciously. The added voting mechanism imposes responsibility to both trainers and voters; an honest voter will have a more significant impact on the overall score of the model updates that will be accepted, while a malicious voter can be identified for collaborating with other malicious trainers, and both will be punished together, accordingly. In addition to the voting and deposit systems, MAM-FL adds a reputation system for participants. This reputation system will track participants' past behaviors, such as their participation and contribution to the network, as well as any negative actions they may have taken. Clients with higher reputations will more likely be selected for model averaging and receive higher rewards. Some aspects of importance that previous works have yet to highlight are integrity and authentication.