# Towards Mobile Federated Learning with Unreliable Participants and Selective Aggregation
## 6 Lessons Learned  
Although the advantages of using FL are obvious, it is also important to note that a solution based on an FL architecture has some potential weaknesses that need to be addressed, such as:  
* The need for communication between clients and server also brings the need to deal with asynchronous client connection or disconnection (client dropout), which will have an impact on the system. For these cases, both the server and the FL architecture need to be extremely resilient. If the system is not prepared to handle connection dropouts or a large influx of users during a communication round, the end result may be significantly degraded or the system may suddenly fail. * The need to protect the privacy of every client also brings with it the need to protect against cyberattacks or malicious clients. Therefore, all communications must be secured, as well as the access to the dataset at each client. * In a federated solution, there is no prior knowledge of the data that each client contains locally. This means that there may be clients that do little to improve the accuracy of the global model. Moreover, in most cases, the clients are heterogeneous, meaning that not all clients have the same computational power or energy efficiency. Accordingly, some clients may take significantly longer to train locally than others. In these cases, the training time in each round depends on the slowest client training and transmitting its local updates. However, FL has very important strengths compared to centralized ML. Above all, client data protection is the focus of the concept presented. Moreover, two main advantages arise: (_i_) distributed training, i.e., it is no longer necessary to have a superpowerful machine on the server to run computationally intensive algorithms, and training becomes easier while being performed in parallel; (_ii_) the need for distributed training implies the decentralized use of data. A federated solution has the potential to use a dataset that grows exponentially with the number of clients connected to the system, i.e., the model becomes more complete and robust and responds more accurately to a wider variety of situations. Based on the analysis performed, this work suggests the inclusion of admission criteria in the implementation of an FL solution. This would allow the introduction of several requirements in the system, for example: \begin{table}
\begin{tabular}{l l l} \hline
**Elapsed Rounds** & **Weighted Average Accuracy** & **Arithmetic Average Accuracy** \\ \hline
10 & 6\% & 2\% \\ \hline
30 & 21\% & 12\% \\ \hline
50 & 32\% & 24\% \\ \hline
100 & 63\% & 51\% \\ \hline \end{tabular}
\end{table} 
Table 6: Results obtained from 8 clients (two clients with 7.5%, two with 12.5%, two with 30%, and two with 0.0002%) with the more complex CIFAR100 dataset. * Ensure that all clients have sufficient computing capacity to make a positive contribution to the training;
* Ensure that all clients have sufficient data to ensure model convergence;
* Ensure that all clients are trustworthy, i.e., do not pose a risk to the privacy of others;
* Ensure that all clients have a stable and secure connection to the server. We strongly believe that the inclusion of admission criteria should be further explored as it would improve the overall quality of an FL architecture. We also believe that FL could benefit from other aggregation methods. A clear example of this is reported in [32], where a protocol with user selection was proposed to reduce the negative effect of correlation within the training data of nearby participants, which impaired the optimization of the FL process. In our work, we demonstrated that using a slightly more complex method improved the overall accuracy of the solution.