## 3 Requirements for Trustability in Federated Learning  
First, multiple requirements to satisfy trustability in a federated learning setting are defined. In each requirement, a discussion is made on how the property has partially been met by existing solutions previously researched. **R1 Confidentiality. **  
Malicious entities can attempt to obtain access to local models and global models by listening to communication channels and then modifying the model data; this involves changing the original owner's data in the model, claiming the model, and then using it for their own gain. Therefore, this requires a solution that can prevent the model from being inferred by the adversaries. One particular solution involves performing encryption [24] during the process of aggregating updates and on the original model itself, with a public key encryption scheme, in such a way that outside entities cannot recognize the contents of the data exchange. **R2 Attractiveness. **  
In FL, the organizer needs to provide a method for consistently recruiting new trainers, as without enough trainers, the resulting global model will not be accurate due to the limited amount of data. Furthermore, having more trainers will increase data diversity, as they will train with different datasets based on their own devices. However, from a trainer's perspective, engaging in local training imposes a cost on their resources. The workers will most likely not perform the FL tasks in the vanilla FL without the motivation to perform the training. To counter this, one author proposed an incentive system to encourage trainers to join the training, mostly via token-based rewards [25]. **R3 Accountability. **  
The major problem with a centralized FL model is that it lacks mechanisms to hold each participant accountable in a fair and objective way [26]. Some trainers can perform training using low-quality datasets on their devices, which can impact the quality of the expected model, and the absence of accountability can be a problem, as this will cause a continuous loop of bad updates from the trainers. The system needs to prepare a solution that can encourage trainers to train with better data while also being capable of detecting and punishing trainers who intentionally manipulate their model accuracy. One author's solution is to impose a reputation system with levels [27], combined with the incentive system that rewards trainers based on their reputations. This encourages trainers to exhibit positive behavior and improve their reputations. If a trainer continues to perform honest training without any detected misbehavior, they will receive increased rewards at the end of the training, while dishonest ones are punished. By imposing this rule, trainers cannot easily remove themselves or reduce their participation, since doing so would diminish the tokens they deposited; this responsibility would compel trainers to continue through the process with good behavior. **R4 Reliability. **  
As the number of trainers increases, a single central server might not have enough capability to handle the communication overhead. Attackers can attempt DDoS (distributed denial-of-service) attacks, which will generate significant overhead for the central server, causing the whole FL system to crash and shut down in the process. The authors of Reference [28] suggested a committee of nodes that could serve as the replacement for the central aggregator. This committee is selected based on a metric system that takes into account factors such as liveliness (the rate at which a node remains available and is not down) and reputation. The most trustworthy nodes will be more likely to be selected as committee members. **R5 Consistency. **  
The lack of trainers with stable internet connectivity might hamper the ability to upload complete parts of their updates since an unreliable network cannot guarantee the full transfer of the update parameters. Malicious trainers can also use this method of dropping and rejoining the model training to intentionally submit low-quality updates.