Demo: FedCampus: A Real-world Privacy-preserving Mobile Application for Smart Campus via Federated Learning & Analytics
This work addresses privacy concerns in smart campus data collection for universities and volunteers, though it is incremental as it applies existing federated learning and analytics methods to a new real-world domain.
The authors tackled the challenge of implementing privacy-preserving smart campus applications by developing FedCampus, a mobile app that uses federated learning and analytics with differential privacy on smartwatches, resulting in successful deployment for tasks like sleep tracking and activity monitoring across 100 devices at Duke Kunshan University.
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.