FedWiLoc: Federated Learning for Privacy-Preserving WiFi Indoor Localization

2025-12-19

AI Summary

To address three key challenges in Wi-Fi indoor localization—privacy leakage, degraded accuracy under multipath conditions, and poor cross-scenario generalizability—this paper proposes a privacy-first federated CSI-based localization framework. Our method employs access point (AP)-side local CSI encoding and embedding vector uploading, integrated with split neural networks and federated learning for collaborative modeling. We further introduce an angle-position joint geometric consistency loss to enforce physical constraints during training. Evaluated across six real-world indoor environments (>2000 sq. ft.), our approach reduces median localization error by 61.9% compared to state-of-the-art methods. Crucially, it guarantees end-to-end privacy preservation throughout both training and inference phases—without requiring raw CSI data sharing. To the best of our knowledge, this is the first work to simultaneously achieve high accuracy, strong privacy guarantees, and broad generalizability across diverse deployment scenarios.

Technology Category

• Machine Learning: Privacy
• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Local Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Security and Privacy: Large-scale security measurements

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.

Problem

• Protects user privacy in WiFi indoor localization
• Improves accuracy in dynamic multipath environments
• Generalizes across different indoor deployments

Innovation

• Split architecture with local CSI processing for privacy
• Federated learning across APs without centralizing user data
• Geometric loss function optimizing AoA and location jointly