# FLIP: A New Approach for Easing the Use of Federated Learning
## 1 Introduction  
The huge and rapid development of artificial intelligence (AI) during the last few years can be considered as the main consequence of the combination of two key factors: the increase in computational power at an affordable cost and the enormous amount of available data due, among other reasons, to the spread of the Internet of Things (IoT) [1]. In an ideal scenario without bureaucratic, ethical, or legal barriers [2], big data could be used to test and develop new tools, applications, and technologies without any kind of restriction and in a rapid way. However, as the gathering and storage of sensitive data is increasing, the need to define new collaborative frameworks that ensure their privacy while allowing their use is vital for not limiting their impact [3].  
In the context of AI, Federated Learning (FL) has been mainly defined with this purpose, that is, to allow the use of massive data based on a privacy-preserving mechanism [4]. Different domains, such as healthcare [5, 6, 7], especially for dealing with critical situations, such as the COVID-19 pandemic [8]; surveillance [9]; or industry [10] have benefited from the development of solutions based on this approach. However, while there are some examples of different FL platforms developed both by academia and industry [7, 11], they tend to be complex solutions with a significant learning curve that requires high IT expertise [12], which limits their wide-scale adoption.  
In order to contribute to reversing this situation, in this paper we present FLIP (Federated Learning Interactive Platform) as a novel approach for an easy-to-use web-based platform that allows real FL networks creation and FL models management, including training and execution. It is specifically focused on reducing the AI engineering load relatedto this kind of deployments and minimising current barriers related to the technical knowledge needed for creating FL solutions in real scenarios, and thus boosting FL adoption. Our evaluation, based on complete functional validation and a SUS questionnaire among 14 users, demonstrates that FLIP enables a fully functional FL system with a high perceived usability level, close to 85%, which can be considered as \"acceptable\" in the acceptability scale, and above industry standard for the industry benchmark.  
The rest of the paper is organised as follows: Section 2 is devoted to analysing the background of the top FL software solutions currently available in both industrial and research fields, with the aim of identifying the main problems and shortcomings that are hindering the adoption of this AI strategy. According to this analysis, this section also presents the core contributions proposed by FLIP. Section 3 is intended to describe our approach, placing special focus on the developed architecture, as well as on aspects related to the network creation and the FL models management. Section 4 presents the functional validation of the solution together with the verification of the achievement of the initial objectives, that is, the analysis of the perceived usability of FLIP. Finally, Section 5 presents the conclusions together with a review of the main limitations and future work for the platform.