Privacy-Preserving Machine Learning Models
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.
Acknowledgement
This report submitted in partial fulfilment of the requirements for the award of the degree of Bachelor of Science in Electrical Engineering and Computer Science under the supervision of Dr. Haroon Lone.