###### Abstract  
The federated learning (FL) approach in machine learning preserves user privacy during data collection. However, traditional FL schemes still rely on a centralized server, making them vulnerable to security risks, such as data breaches and tampering of models caused by malicious actors attempting to gain access by masquerading as trainers. To address these issues that hamper the trustability of federated learning, requirements were analyzed for several of these problems. The findings revealed that issues, such as the lack of accountability management, malicious actor mitigation, and model leakage, remained unaddressed in prior works. To fill this gap, a blockchain-based trustable FL scheme, MAM-FL, is proposed with the focus on providing accountability to trainers. MAM-FL established a group of voters responsible for evaluating and verifying the validity of the model updates submitted. The effectiveness of MAM-FL was tested based on the reduction of malicious actors present on both trainers' and voters' sides and the ability to handle colluding participants. Experiments show that MAM-FL succeeded at reducing the number of malicious actors, despite the test case involving initial collusion in the system. federated learning; blockchain; trustability +
Footnote †: journal: Medical  
until the end. For example, a hacker might gain access to a trainer's account and use its identity to inject bad updates to the model, while masquerading as a normal, uncompromised account. There is also the unmentioned problem of model leakage during transmission, which can allow attackers to access the data by eavesdropping on the communication channels, or masquerading as a participant in the scheme. In order to tackle the problems, such as the lack of accountability management, malicious actor prevention, and model leakage, MAM-FL, a scheme that addresses vulnerabilities, is proposed. First, in MAM-FL, signcryption is adopted to prevent model and privacy leakage by providing encryption, signature authentication, and integrity verification. An accountability-checking mechanism is integrated through a party of voters to verify the accuracy claims of trainers. Adding a verifying party will create a need for trainers to assume responsibility, which provides accountability and ensures consistency in the quality of the model updates. MAM-FL consists of an initial setup, leading up to the creation of the next global model, in which the selected model updates are chosen through the result of ranking the scores in the model updates. Throughout MAM-FL, blockchain and the InterPlanetary File System (IPFS) [17] act as communication channels to provide reliability and resistance toward single-point-of-failure (SPoF) caused by DDoS attacks and spam, which is a property of a decentralized system. Finally, to motivate participants to join and then perform honestly, a reward system is proposed, which grows proportionately based on the participant's initial deposit. The following contributions are provided in this paper:  
1. We address several concerns of traditional federated learning and formulate seven requirements based on existing solutions. We provide reasoning to explain how MAM-FL can fix the problems mentioned in the seven requirements. 2. Signcryption is integrated into the initial model transmission to provide confidentiality; blockchain provides reliable protection against single points of failure and verifies the integrity of data during communication. 3. An FL protocol utilizing a voting-based mechanism is proposed. The voting system is combined with a dynamic reputation system that affects the weight of the votes. The purpose is to introduce accountability measures that can detect and prevent malicious actors from jeopardizing the FL ecosystem. The rest of this paper is organized as follows. First, the definitions are listed in Section 2. In Section 3, seven proposed requirements are shown, along with the state-of-the-art blockchain-based FL protocols.