FedWiLoc: Federated Learning for Privacy-Preserving WiFi Indoor Localization

TL;DR

FedWiLoc is a novel federated learning-based indoor localization system that enhances privacy and accuracy in Wi-Fi environments by processing data locally and collaboratively training models without exposing raw data.

Contribution

It introduces a split architecture with local CSI processing, a federated training approach, and a geometric loss function to improve privacy and localization accuracy in multipath environments.

Findings

Outperforms state-of-the-art by up to 61.9% in median error

Maintains strong privacy guarantees during training and inference

Effective across diverse indoor environments

Abstract

Current data-driven Wi-Fi-based indoor localization systems face three critical challenges: protecting user privacy, achieving accurate predictions in dynamic multipath environments, and generalizing across different deployments. Traditional Wi-Fi localization systems often compromise user privacy, particularly when facing compromised access points (APs) or man-in-the-middle attacks. As IoT devices proliferate in indoor environments, developing solutions that deliver accurate localization while robustly protecting privacy has become imperative. We introduce FedWiLoc, a privacy-preserving indoor localization system that addresses these challenges through three key innovations. First, FedWiLoc employs a split architecture where APs process Channel State Information (CSI) locally and transmit only privacy-preserving embedding vectors to user devices, preventing raw CSI exposure. Second, during training, FedWiLoc uses federated learning to collaboratively train the model across APs without centralizing sensitive user data. Third, we introduce a geometric loss function that jointly optimizes angle-of-arrival predictions and location estimates, enforcing geometric consistency to improve accuracy in challenging multipath conditions. Extensive evaluation across six diverse indoor environments spanning over 2,000 sq. ft. demonstrates that FedWiLoc outperforms state-of-the-art methods by up to 61.9% in median localization error while maintaining strong privacy guarantees throughout both training and inference.