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

Jiaxiang Geng, Beilong Tang, Boyan Zhang, Jiaqi Shao, Bing Luo
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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.
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演示:FedCampus:通过联合学习与分析为智慧校园提供的真实世界隐私保护移动应用程序
在本演示中,我们将介绍 FedCampus,这是一款保护隐私的移动应用程序,用于智能联合学习(FL)和联合分析(FA)。FedCampus 可在 iOS 和 Android 设备上实现跨平台 FL/FA,支持连续模型和算法部署(MLOps)。我们的应用程序通过智能手表的差分隐私(DP)集成了隐私保护处理数据,处理后的参数通过 FedCampus 后端平台用于 FL/FA。我们向昆山杜克大学的志愿者分发了 100 块智能手表,并成功完成了一系列智慧校园任务,包括睡眠跟踪、体力活动监测、个性化推荐和重击等功能。我们的项目开源于 https://github.com/FedCampus/FedCampus_Flutter.See,FedCampus 视频开源于 https://youtu.be/k5iu46IjA38。
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