research-article

PMF: A Privacy-preserving Human Mobility Prediction Framework via Federated Learning

Authors:

Diansheng Guo, and

Yong LiAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 4, Issue 1

Article No.: 10, Pages 1 - 21

https://doi.org/10.1145/3381006

Published: 18 March 2020 Publication History

Abstract

With the popularity of mobile devices and location-based social network, understanding and modelling the human mobility becomes an important topic in the field of ubiquitous computing. With the model developing from personal models with own information to the joint models with population information, the prediction performance of proposed models become better and better. Meanwhile, the privacy issues of these models come into the view of community and the public: collecting and uploading private data to the centralized server without enough regulation. In this paper, we propose PMF, a privacy-preserving mobility prediction framework via federated learning, to solve this problem without significantly sacrificing the prediction performance. In our framework, based on the deep learning mobility model, no private data is uploaded into the centralized server and the only uploaded thing is the updated model parameters which are difficult to crack and thus more secure. Furthermore, we design a group optimization method for the training on local devices to achieve better trade-off between performance and privacy. Finally, we propose a fine-tuned personal adaptor for personal modelling to further improve the prediction performance. We conduct extensive experiments on three real-life mobility datasets to demonstrate the superiority and effectiveness of our methods in privacy protection settings.

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PMF: A Privacy-preserving Human Mobility Prediction Framework via Federated Learning

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 4, Issue 1

March 2020

1006 pages

EISSN:2474-9567

DOI:10.1145/3388993

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Copyright © 2020 ACM.

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Publication History

Published: 18 March 2020

Published in IMWUT Volume 4, Issue 1

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Park HYoon S(2024)Deep reinforcement learning for base station switching scheme with federated LSTM‐based traffic predictionsETRI Journal10.4218/etrij.2023-0065Online publication date: 12-Jan-2024
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Belal YBen Mokhtar SHaddadi HWang JMashhadi A(2024)Survey of Federated Learning Models for Spatial-Temporal Mobility ApplicationsACM Transactions on Spatial Algorithms and Systems10.1145/366608910:3(1-39)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1145/3666089
Zeng JZhang GYuan JLi YJin D(2024)Empowering Predictive Modeling by GAN-based Causal Information LearningACM Transactions on Intelligent Systems and Technology10.1145/365261015:3(1-19)Online publication date: 17-May-2024
https://dl.acm.org/doi/10.1145/3652610
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