Federated Learning-Based Localization with Heterogeneous Fingerprint Database

TL;DR

This paper introduces a federated learning-based indoor localization method that effectively handles database heterogeneity, improving prediction accuracy in diverse scenarios while preserving privacy.

Contribution

The paper proposes FedLoc-AC, a novel federated localization algorithm that accounts for database heterogeneity using convex hull metrics, enhancing performance over existing methods.

Findings

FedLoc-AC outperforms FedLoc in heterogeneous scenarios.

FedLoc-AC maintains similar accuracy to FedLoc in homogeneous scenarios.

Extension of FedLoc-AC to multi-floor environments is validated.

Abstract

Fingerprint-based localization plays an important role in indoor location-based services, where the position information is usually collected in distributed clients and gathered in a centralized server. However, the overloaded transmission as well as the potential risk of divulging private information burdens the application.Owning the ability to address these challenges, federated learning (FL)-based fingerprinting localization comes into people's sights, which aims to train a global model while keeping raw data locally. However, in distributed machine learning (ML) scenarios, the unavoidable database heterogeneity usually degrades the performance of existing FL-based localization algorithm (FedLoc). In this paper, we first characterize the database heterogeneity with a computable metric, i.e., the area of convex hull, and verify it by experimental results. Then, a novel heterogeneous FL-based localization algorithm with the area of convex hull-based aggregation (FedLoc-AC) is proposed. Extensive experimental results, including real-word cases are conducted. We can conclude that the proposed FedLoc-AC can achieve an obvious prediction gain compared to FedLoc in heterogeneous scenarios and has almost the same prediction error with it in homogeneous scenarios. Moreover, the extension of FedLoc-AC in multi-floor cases is proposed and verified.