RiM: Record, Improve and Maintain Physical Well-being using Federated Learning
This addresses health challenges for students in academic environments by offering a privacy-preserving solution, though it is incremental as it applies existing federated learning methods to a specific domain.
The study tackled the problem of declining physical well-being among students in academic settings by developing a mobile app that uses federated learning to analyze lifestyle habits and provide personalized recommendations, achieving an average accuracy of 60.71% and mean absolute error of 0.91 while preserving privacy.
In academic settings, the demanding environment often forces students to prioritize academic performance over their physical well-being. Moreover, privacy concerns and the inherent risk of data breaches hinder the deployment of traditional machine learning techniques for addressing these health challenges. In this study, we introduce RiM: Record, Improve, and Maintain, a mobile application which incorporates a novel personalized machine learning framework that leverages federated learning to enhance students' physical well-being by analyzing their lifestyle habits. Our approach involves pre-training a multilayer perceptron (MLP) model on a large-scale simulated dataset to generate personalized recommendations. Subsequently, we employ federated learning to fine-tune the model using data from IISER Bhopal students, thereby ensuring its applicability in real-world scenarios. The federated learning approach guarantees differential privacy by exclusively sharing model weights rather than raw data. Experimental results show that the FedAvg-based RiM model achieves an average accuracy of 60.71% and a mean absolute error of 0.91--outperforming the FedPer variant (average accuracy 46.34%, MAE 1.19)--thereby demonstrating its efficacy in predicting lifestyle deficits under privacy-preserving constraints.