SAFELOC: Overcoming Data Poisoning Attacks in Heterogeneous Federated Machine Learning for Indoor Localization
Akhil Singampalli, Danish Gufran, Sudeep Pasricha · 2025
Machine learning (ML) based indoor localization solutions are critical for many emerging applications, yet their efficacy is often compromised by hardware/software variations across mobile devices (i.e., device heterogeneity) and the threat of ML data poisoning attacks. Conventional methods aimed at countering these challenges show limited resilience to the uncertainties created by these phenomena. In response, we introduce SAFELOC, a novel framework that not only minimizes localization errors under these challenging conditions but also ensures model compactness for efficient mobile device deployment. SAFELOC introduces a novel fused neural network architecture that performs data poisoning detection and localization, with a low model footprint using federated learning (FL). Additionally, a dynamic saliency map-based aggregation strategy is designed to adapt based on the severity of the detected data poisoning scenario. Experimental evaluations demonstrate that SAFELOC achieves improvements of up to 5.9× in mean localization error, 7.8× in worst-case localization error, and a 2.1× reduction in model inference latency compared to state-of-the-art indoor localization frameworks across diverse indoor environments and data poisoning attack scenarios.