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

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 Then, we demonstrate various practical use cases that are summarized from a mixture of standard, newly published, and unpublished works, which cover a broad range of location services, including collaborative static localization/fingerprinting, indoor target tracking, outdoor navigation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.

Federated Learning for Internet of Things: A Comprehensive Survey

“…An experiment is conduced in a laboratory corridor setting, indicating a high localization estimation accuracy with high security. Similarly, a Federated Localization (FedLoc) framework is considered in [91] for providing accurate localization services in IoT networks. By cooperating multiple users in the model learning using local fingerprint data, FL minimizes the bias of location estimation with privacy protections.…”

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues

“…Considering various channel models, i.e., path-loss, shadowing, and fading, the proposed solutions can achieve Neyman-Pearson optimal performance by observing the probability of miss-detections and false-alarms. The authors in [154] propose a novel localization system leveraging the federated learning to allow mobile users to collaboratively provide accurate location services without revealing mobile users’ private location. As such, the authors utilize deep neural networks with the Gaussian process to accurately predict the desired location of the mobile users.…”

Graph Neural Network for Large-Scale Network Localization

“…The above signal model well suits many realistic large-scale wireless networks. For instance, in 5G network, a large number of small base stations are densely deployed in each cell; Internet of Things (IoT) network, advocating interconnection of everything, comprises a huge number of connected smart devices and machines [19]. For large-scale networks, it is typical that only a small fraction of nodes know their locations precisely.…”