@article{3188309,
    title = "FedLoc: Federated Learning Framework for Data-Driven Cooperative
Localization and Location Data Processing",
    author = "Yin, Feng and Lin, Zhidi and Kong, Qinglei and Xu, Yue and Li, Deshi and and Theodoridis, Sergios and Cui, Shuguang Robert",
    journal = "IEEE OPEN JOURNAL OF SIGNAL PROCESSING",
    year = "2020",
    volume = "1",
    pages = "187-215",
    publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",
    doi = "10.1109/OJSP.2020.3036276",
    keywords = "Cooperation; data-driven models; distributed processing; federated
learning; Gaussian processes; location services; user privacy",
    abstract = "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newly published, and unpublished works, which cover a broad range of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."
}