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

Scientific publication - Journal Article uoadl:3188309 36 Read counter

Unit:
NKUA research material
Title:
FedLoc: Federated Learning Framework for Data-Driven Cooperative
Localization and Location Data Processing
Languages of Item:
English
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newly published, and unpublished works, which cover a broad range of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.
Publication year:
2020
Authors:
Yin, Feng
Lin, Zhidi
Kong, Qinglei
Xu, Yue
Li, Deshi and
Theodoridis, Sergios
Cui, Shuguang Robert
Journal:
IEEE OPEN JOURNAL OF SIGNAL PROCESSING
Publisher:
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Volume:
1
Pages:
187-215
Keywords:
Cooperation; data-driven models; distributed processing; federated
learning; Gaussian processes; location services; user privacy
Official URL (Publisher):
DOI:
10.1109/OJSP.2020.3036276
The digital material of the item is not available.