# Towards Mobile Federated Learning with Unreliable Participants and Selective Aggregation
## 7 Conclusions  
This paper presented a multimobile Android-based implementation of a federated learning solution and studied the impact of unreliable participants and selective aggregation in the federated solution towards the deployment of mobile FL solutions in real-world applications. After establishing a baseline using a traditional ML approach, we showed that we were able to achieve similar performance using an FL solution with two clients and a uniformly distributed dataset. When the dataset was not evenly distributed among participants, a lower accuracy was observed in the early stages of training, and the network typically required a higher number of rounds before performance converged. After the introduction of a weighted average selective aggregation, we found a significant improvement in convergence when using a dataset unevenly distributed among clients. However, the accuracy achieved for four and eight clients after the 100 rounds of training was not different from the previous tests. This was due to the complexity and dimension of the dataset, which led to a training accuracy near the accuracy that was close to the known maximum accuracy when enough training rounds were performed. Afterwards, we ran cross-validation tests to demonstrate the generalizable nature of our results. Finally, we conducted experiments with a more complex dataset and found that using the weighted average aggregation method not only improved the accuracy across rounds but also improved the maximum accuracy achieved after 100 rounds of training. By comparing an arithmetic average aggregation method and a weighted average aggregation method, we concluded that there was room for improvement in the aggregation methods used for FL. In the context of the existing challenges of FL, we also concluded that FL would benefit from the introduction of admission criteria for clients. For future work, it would be important to test the implemented solution for a larger number of clients (i.e., higher than eight) and further analyze the impact of the number of clients in the training process and communications. In addition, our experience has shown that the proper handling of disconnections and newly added clients during training is critical for real-world deployment. Other opportunities to be explored include using heterogeneous clients with different computational capacities, investigating the impact of clients with unfavorable data (e.g., blurred or pixelated images), improving the aggregation method to further optimize model convergence, testing the federated solution with even larger and more complex datasets to validate the applicability of the model to other use cases, and exploring cybersecurity techniques and secure connections for privacy preservation to enable sustainability for all participants. Conceptualization, D.P., P.P. and G.F.; methodology, L.E., D.P., P.P. and G.F.; software, L.E. ; validation, L.E. ; formal analysis, L.E., D.P., P.P. and G.F.; investigation, L.E. ; resources, D.P., P.P. and G.F.; data curation, L.E. ; writing--original draft preparation, L.E. and D.P. ; writing--review and editing, D.P., P.P. and G.F.; visualization, L.E. and D.P. ; supervision, D.P., P.P. and G.F.; project administration, G.F.; funding acquisition, D.P., P.P. and G.F. All authors have read and agreed to the published version of the manuscript. This work was supported by Instituto de Telecomunicacoes and Fundacao para a Ciencia e a Tecnologia, Portugal, under grants UIDB/50008/2020 and EXPL/EEI-HAC/1511/2021. This work was also supported by Instituto de Sistemas e Robotica (Project UIDB/00048/2020), funded by the Portuguese Science Agency \"Fundacao para a Ciencia e a Tecnologia\" (FCT). Not applicable. The data presented in this study are available on request from the first author. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: