# FLIP: A New Approach for Easing the Use of Federated Learning
## 2 State of the Art and Contributions  
As mentioned above, in recent years, the capacity for data generation, storage, and computation has increased significantly [13]. Regarding this, the concern for privacy in the transmission of raw data has led to the development of new strategies that make use of local capabilities to avoid such transmission and, thus, limit the security breaches that it could entail, but without renouncing the use of massive data in obtaining machine learning models. This is the premise behind FL, whose main objective is to explore the training of statistical models directly on remote devices [14].  
There are several software alternatives that support FL in research and industrial fields. In the research domain, LEAF [15] provides sample implementations and simulates statistical heterogeneity with federated datasets, but it comes with a lack of deployability due to its complexity of use. TensorflowFederated (TFF) [16] is also a research-orientated platform with two layers of APIs, but, on the one hand, it does not provide real FL functionality, since it only simulates the multinode setup in a single machine, and on the other hand, it requires using (and knowing) Tensorflow [17] and Keras [18] as the basis for producing algorithms. Similarly, a more recent approach is EasyFL [12] which is defined as easy-to-use software that sequentially executes a simulation on a single machine but does not allow real deployment of the FL network. As can be seen, these platforms lack the capability to build FL prototypes and run real FL in production. Moreover, except for EasyFL, the other approaches are not user-friendly enough to help with FL use. In the industrial field, Fate [19] and PaddleFL [20] are deployable with containerisation and are designed for real FL scenarios, but they are complex systems for beginners that require deep knowledge of deployment and usage. Fed-BioMed [21] targets hospitals and clinical studies, but presents some limitations in terms of generalisation to other cases. Finally, Flower [22] is a complete platform that not only supports simulation, but also executes real nodes in a federated setup, while it is model-agnostic, but it does not provide a user-friendly way to interact with to create the FL network and algorithms. In this case, industrial approaches allow the deployment of real FL in production, but they lack usability for widespread adoption. Table 1 summarizes the information presented in this paragraph for the most promising solutions.  
According to this analysis, the main challenge regarding current FL software solutions is to be able to provide a fully functional platform for deploying FL models in real networks in an easy manner. Taking this into account, our evaluation demonstrates that FLIP can be considered as a usable tool to create FL networks and then use them to train FL algorithms as expected. To do so, our main contributions can be summarised as follows.  
* We provide a platform that is capable of deploying a real multi-node network for FL training and execution.
* We identify two different roles with regard to the usage of the platform in order to simplify its operation: the platform administrator and the data scientist. The former is in charge of deploying the different nodes, providing the access permissions, and managing the FL network, while the latter just need to be focused on the development of the FL algorithms and then to seamlessly run them in the network.
* The proposed platform is based on web interfaces to facilitate user interaction.
* All the modules of the platform are easily deployable, the administrator should set up an environment file (.env), and a bash script takes care of the rest.  
Following this approach, the intended ease of use of FLIP is reflected in the results obtained for the SUS questionnaire, which is explained further in the fourth section.