FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing

In this overview paper, data-driven learning model-based cooperative localization and location data processing are considered, in line with the emerging machine learning and big data methods. We first review (1) state-of-the-art algorithms in the context of federated learning, (2) two widely used le...

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Bibliographic Details
Published in:IEEE open journal of signal processing 2020, Vol.1, p.187-215
Main Authors: Yin, Feng, Lin, Zhidi, Kong, Qinglei, Xu, Yue, Li, Deshi, Theodoridis, Sergios, Cui, Shuguang Robert
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Collaboration
Collaborative work
Context modeling
Cooperation
Data models
Data processing
data-driven models
distributed processing
Federated learning
Fingerprinting
Gaussian process
Gaussian processes
Localization
location services
Machine learning
Optimization
Satellite navigation systems
State-of-the-art reviews
Tracking
Traffic information
Traffic models
user privacy
Wireless communication
Wireless sensor networks
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Summary:In this overview paper, data-driven learning model-based cooperative localization and location data processing are considered, in line with the emerging machine learning and big data methods. We first review (1) state-of-the-art algorithms in the context of federated learning, (2) two widely used learning models, namely the deep neural network model and the Gaussian process model, and (3) various distributed model hyper-parameter optimization schemes. Then, we demonstrate various practical use cases that are summarized from a mixture of standard, newly published, and unpublished works, which cover a broad range of location services, including collaborative static localization/fingerprinting, indoor target tracking, outdoor navigation using low-sampling GPS, and spatio-temporal wireless traffic data modeling and prediction. Experimental results show that near centralized data fitting- and prediction performance can be achieved by a set of collaborative mobile users running distributed algorithms. All the surveyed use cases fall under our newly proposed Federated Localization (FedLoc) framework, which targets on collaboratively building accurate location services without sacrificing user privacy, in particular, sensitive information related to their geographical trajectories. Future research directions are also discussed at the end of this paper.
ISSN:2644-1322
2644-1322
DOI:10.1109/OJSP.2020.3036276