# FLIP: A New Approach for Easing the Use of Federated Learning
## 5 Conclusions and Future Work  
Distributed, privacy-preserved machine learning is a hot topic at the moment as regulation is shifting towards private, strictly governed data environments (GDPR, PIPL, etc. ), and the impact is even higher in the case of sensitive datasets, as in the clinical field. One of the ways to comply with these constraints is to leverage the use of federated learning systems, or federated systems more broadly, where the data reside in private and secured data storage centres, and the users never interact with it directly, but in an indirect way. For example, a researcher can conduct statistical analysis (aggregations) of the datasets without knowing the individualities, and thus violating the policies; another application consists of introducing algorithms which directly interact with data and learn patterns  
Figure 6: SUS scores for both systems, Flower and our platform. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Flower** & **Our Platform** \\ \hline SUS Score (mean) & 60.36 & 84.64 \\ SD & 25.39 & 12.49 \\ Min & 17.5 & 52.5 \\ Max & 95 & 100 \\ Percentile value & 28.18 & 95.15 \\
1. Quartile & 36.25 & 76.87 \\ Median & 57.5 & 85 \\
3. Quartile & 85.62 & 95 \\ Quartile scale & 1 st & 4 th \\ Acceptability Scale & Marginal & Acceptable \\ NPS Scale & Detractor & Promoter \\ Industry Benchmark & Below average & Above industry Standard \\ \hline \hline \end{tabular}
\end{table} 
Table 5: SUS questionnaire results comparison. from it, and even consistently leveraging this feature in a multinode multidata setting to improve the dataset availability. In this regard, there have been several attempts to build and introduce federated learning systems to the research community, keeping away the bridge between testing environments, where researchers have direct access to data, and production-ready systems, where researchers need to be allowed to specific data sources. We have designed and implemented FLIP, where privacy and security are taken into account as mandatory requirements of the software, while keeping the entry barrier for research as simple as possible. This way, we provide an easy to use web-based platform that can differentiate between two clear users, administrator and researcher, where the first is the unique person that can manage the system and may have direct contact with the data. FLIP has demonstrated a low entry barrier, as shown in the SUS results, while keeping the performance almost in sync with respect to isolated training processes. We have proved that RAM usage keeps almost as low as in the traditional approach and that time is dependent on the hardware resources of the worst node. One way to potentially solve this issue could be to train the least powerful nodes once every several rounds, but this and other alternatives are kept for future studies. FLIP is currently ongoing further changes within H2020 projects, and thus we have decided to keep it as a private repository, also to comply with IPR and project needs. We are planning to have a public release once we can show a high TRL (Technology Readiness Level). Finally, within the scope of future work, even though the platform provides a functional tool, the set of production-ready services is just a few. We have aimed towards a platform covering the basic functionalities and performing correctly, rather than trying to achieve a complex, full-featured design, both because we have considered an incremental delivery process, and because decisions should happen carefully in order to ensure a steady step. In this way, we have established the basis for potential use cases that enhance the true potential of federated learning through an easy-to-use GUI. Furthermore, there are several possibilities to extend current state, which are, among others, the adoption of current standard tools to deliver new functionalities in a production-ready environment, the integration of ETL (Extract, Transform, and Load) and data storage services, and promoting the user experience to an even higher level.