PRIVACY-PRESERVING ON-SCREEN ACTIVITY TRACKING AND CLASSIFICATION IN E-LEARNING USING FEDERATED LEARNING (KEY IJP************223)

• Mrs.m.anusha,M.bhavana,N.pranathi,T.likhitha,T.vigna Sree

Abstract

In the evolving landscape of remote and online learning, the ability to monitor and assess studentsproductivity has become increasingly. This project is used for "Privacy-Preserving On-Screen Activity Tracking and Classification in E-Learning Using Federated Learning." It aims whether students are utilizing their time for knowledge development or wasting it. E-learning platforms have gained popularity, especially in remote education. However, students are actively focused during online sessions. Our approach uses Federated Learning, to user privacy while accurately classifying onscreen activities. By this technique, we address the challenge of preserving user privacy and providing valuable insights into the efficiency of online learning. Federated Learning uses to our system to train machine learning models across multiple user devices, eliminating the need to centralize sensitive data on a single server.