Demo: FedCampus: A Real-world Privacy-preserving Mobile Application for Smart Campus via Federated Learning & Analytics

TL;DR

FedCampus is a privacy-preserving mobile app utilizing federated learning and analytics on smart campus data from smartwatches, enabling cross-platform deployment and continuous model updates while maintaining user privacy.

Contribution

This work introduces FedCampus, a novel mobile application that integrates federated learning and analytics for smart campus data with differential privacy, supporting cross-platform deployment and continuous updates.

Findings

Successfully deployed on 100 smartwatches at Duke Kunshan University.

Enabled sleep tracking, activity monitoring, and personalized recommendations.

Demonstrated effective privacy-preserving federated analytics in a real-world campus setting.

Abstract

In this demo, we introduce FedCampus, a privacy-preserving mobile application for smart \underline{campus} with \underline{fed}erated learning (FL) and federated analytics (FA). FedCampus enables cross-platform on-device FL/FA for both iOS and Android, supporting continuously models and algorithms deployment (MLOps). Our app integrates privacy-preserving processed data via differential privacy (DP) from smartwatches, where the processed parameters are used for FL/FA through the FedCampus backend platform. We distributed 100 smartwatches to volunteers at Duke Kunshan University and have successfully completed a series of smart campus tasks featuring capabilities such as sleep tracking, physical activity monitoring, personalized recommendations, and heavy hitters. Our project is opensourced at https://github.com/FedCampus/FedCampus_Flutter. See the FedCampus video at https://youtu.be/k5iu46IjA38.