RiM: Record, Improve and Maintain Physical Well-being using Federated Learning

TL;DR

This paper presents RiM, a federated learning-based mobile app that personalizes health recommendations for students while preserving privacy, demonstrating improved prediction accuracy over existing methods.

Contribution

It introduces a novel federated learning framework for personalized health recommendations that ensures privacy and is validated on real student data.

Findings

RiM achieves 60.71% accuracy in predicting lifestyle deficits.

Federated learning with model weight sharing preserves privacy effectively.

The approach outperforms non-federated variants in accuracy and error metrics.

Abstract

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.