Empowering e-learning approach by the use of federated edge computing

Abstract

Federated learning (FL) is a decentralized approach to training machine learning model. In the traditional architecture, the training requires getting the whole data what causes a threat to the privacy of the sensitive data. FL was proposed to overcome the cited limits. The principal of FL revolves around training machine learning models locally on individual devices instead of gathering all the data in a central server, and only the updated models are shared and aggregated. Concerning e-learning, it is about using electronic/digital technology to deliver educational content in order to facilitate the learning. It becomes popular with the advancement of the internet and digital devices mainly after the COVID-19. In this work, we propose an e-learning recommendation system based on FL architecture where we can propose suitable courses to the learner. Because of the important number of connected learners looking for online courses, the FL encounters a problem: bottleneck communication. This situation can cause the increase of the computational load, the longer time of the aggregation, the saturation of the resources, etc. As solution, we propose using the edge computing potentials so that the aggregation will be performed first in the edge layer then in the central server, reducing hence, the need for continuous data transmission to the server and enabling a faster inference while keeping the security and privacy of the data. The experiments carried out prove the effectiveness of our approach in solving the problem addressed in this work.