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

In this paper, we propose a new localization framework in which mobile users or smart agents can cooperate to build accurate location services without sacrificing privacy, in particular, information related to their trajectories. The proposed framework is called Federated Localization (FedLoc), simply because it adopts the recently proposed federated learning. Apart from the new FedLoc framework, this paper can be deemed as an overview paper, in which we review the state-of-the-art federated learning framework, two widely used learning models, various distributed model hyper-parameter optimization schemes, and some practical use cases that fall under the FedLoc framework. The use cases, summarized from a mixture of standard, recently published, and unpublished works, cover a broad range of location services, including collaborative static localization/fingerprinting, indoor target tracking, outdoor navigation using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. The obtained primary results confirm that the proposed FedLoc framework well suits data-driven, machine learning-based localization and spatio-temporal data modeling. Future research directions are discussed at the end of this paper.