@inproceedings{fedf1ab1d29d4de59b1883c9657bb8b7,
title = "Federated Meta-Location Learning for Fine-Grained Location Prediction",
abstract = "Fine-grained location prediction on smart phones can be used to improve app/system performance. Application scenarios include video quality adaptation as a function of the 5G network quality at predicted user locations, and augmented reality apps that speed up content rendering based on predicted user locations. Such use cases require prediction error in the same range as the GPS error, and no existing works on location prediction can achieve this level of accuracy. We propose Federated Meta-Location Learning (FMLL) on smart phones for fine-grained location prediction, based on GPSt races collected on the phones. FMLL has three components: a meta-location generation module, a prediction model, and a federated learning framework. The meta-location generation module represents the user location data as relative points in an abstract 2D space, which enables learning across different physical spaces. The model fuses Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Networks (CNN), where BiLSTM learns the speed and direction of the mobile users, and CNN learns information such as user movement preferences. The framework runs on the phones of the users and also on a server that coordinates learning from all users in the system. FMLL uses federated learning to protect user privacy and reduce bandwidth consumption. Our experimental results, using a dataset with over 600,000 users, demonstrate that FMLL outperforms baseline models in terms of prediction accuracy. We also demonstrate that FMLL works well in conjunction with transfer learning, which enables model reusability. Finally, benchmark results on Android phones demonstrate FMLL's feasibility in real life.",
keywords = "deep learning, federated learning, location prediction, smart phones",
author = "Xiaopeng Jiang and Shuai Zhao and Guy Jacobson and Rittwik Jana and Hsu, {Wen Ling} and Manoop Talasila and Aftab, {Syed Anwar} and Yi Chen and Cristian Borcea",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Big Data, Big Data 2021 ; Conference date: 15-12-2021 Through 18-12-2021",
year = "2021",
doi = "10.1109/BigData52589.2021.9671447",
language = "English (US)",
series = "Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "446--456",
editor = "Yixin Chen and Heiko Ludwig and Yicheng Tu and Usama Fayyad and Xingquan Zhu and Hu, {Xiaohua Tony} and Suren Byna and Xiong Liu and Jianping Zhang and Shirui Pan and Vagelis Papalexakis and Jianwu Wang and Alfredo Cuzzocrea and Carlos Ordonez",
booktitle = "Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021",
address = "United States",
}