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

2024/08/31 by Jiaxiang Geng, Geng, Jiaxiang, Beilong Tang +7 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Cryptography and Security (cs.CR) #Distributed #FOS: Computer and information sciences #IoT and Edge/Fog Computing #Parallel #Privacy-Preserving Technologies in Data #and Cluster Computing (cs.DC)

paper · pdf · doi:10.48550/arxiv.2409.00327

openalex publication_date 2024/08/31 · openalex created_date 2024/09/29 · openalex updated_date 2026/07/28

Abstract

In this demo, we introduce FedCampus, a privacy-preserving mobile application for smart \underlinecampus with \underlinefederated 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.

Cited by

• Adaptive Federated LoRA in Heterogeneous Wireless Networks with Independent Sampling

Related

• FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps
• APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service
• Federated Learning: A Survey on Privacy-Preserving Collaborative Intelligence
• PAPAYA Federated Analytics Stack: Engineering Privacy, Scalability and Practicality
• Privacy-Preserving Blockchain Based Federated Learning with Differential Data Sharing