用联邦学习保护隐私，实现校园智能设备的本地化数据分析。
Demo: FedCampus: A Real-world Privacy-preserving Mobile Application for Smart Campus via Federated Learning & Analytics
- 跨平台在手机端运行联邦学习与分析，支持持续模型部署。
- 通过差分隐私处理智能手表数据，完成睡眠、运动等任务。
- 适合关注隐私保护与智能校园落地的开发者与研究者。
本演示介绍 FedCampus，一个基于联邦学习（FL）和联邦分析（FA）的隐私保护型智能校园移动应用。该应用支持 iOS 与 Android 双平台的设备端联邦学习/分析，并实现模型与算法的持续部署（MLOps）。系统通过差分隐私（DP）处理来自智能手表的敏感数据，经本地处理后用于联邦学习与分析。我们在杜克-昆山大学发放了 100 个智能手表给志愿者，成功完成了包括睡眠追踪、身体活动监测、个性化推荐及热点用户识别在内的多项智能校园任务。项目已开源，代码地址：https://github.com/FedCampus/FedCampus_Flutter，视频演示见：https://youtu.be/k5iu46IjA38。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。