Jiaxiang Geng, Beilong Tang, Boyan Zhang, Jiaqi Shao, Bing Luo
{"title":"Demo: FedCampus: A Real-world Privacy-preserving Mobile Application for Smart Campus via Federated Learning & Analytics","authors":"Jiaxiang Geng, Beilong Tang, Boyan Zhang, Jiaqi Shao, Bing Luo","doi":"arxiv-2409.00327","DOIUrl":null,"url":null,"abstract":"In this demo, we introduce FedCampus, a privacy-preserving mobile application\nfor smart \\underline{campus} with \\underline{fed}erated learning (FL) and\nfederated analytics (FA). FedCampus enables cross-platform on-device FL/FA for\nboth iOS and Android, supporting continuously models and algorithms deployment\n(MLOps). Our app integrates privacy-preserving processed data via differential\nprivacy (DP) from smartwatches, where the processed parameters are used for\nFL/FA through the FedCampus backend platform. We distributed 100 smartwatches\nto volunteers at Duke Kunshan University and have successfully completed a\nseries of smart campus tasks featuring capabilities such as sleep tracking,\nphysical activity monitoring, personalized recommendations, and heavy hitters.\nOur project is opensourced at https://github.com/FedCampus/FedCampus_Flutter.\nSee the FedCampus video at https://youtu.be/k5iu46IjA38.","PeriodicalId":501422,"journal":{"name":"arXiv - CS - Distributed, Parallel, and Cluster Computing","volume":"25 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Distributed, Parallel, and Cluster Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.00327","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.