4 papers
ARMOR: Adaptive Resilience Against Model Poisoning Attacks in Continual Federated Learning for Mobile Indoor Localization
Danish Gufran, Akhil Singampalli, Sudeep Pasricha
Indoor localization has become increasingly essential for applications ranging from asset tracking to delivering personalized services. Federated learning (FL) offers a privacy-pre…
Unified Class and Domain Incremental Learning with Mixture of Experts for Indoor Localization
Akhil Singampalli, Sudeep Pasricha
Indoor localization using machine learning has gained traction due to the growing demand for location-based services. However, its long-term reliability is hindered by hardware/sof…
DAILOC: Domain-Incremental Learning for Indoor Localization using Smartphones
Akhil Singampalli, Danish Gufran, Sudeep Pasricha
Wi-Fi fingerprinting-based indoor localization faces significant challenges in real-world deployments due to domain shifts arising from device heterogeneity and temporal variations…
SAFELOC: Overcoming Data Poisoning Attacks in Heterogeneous Federated Machine Learning for Indoor Localization
Akhil Singampalli, Danish Gufran, Sudeep Pasricha
Machine learning (ML) based indoor localization solutions are critical for many emerging applications, yet their efficacy is often compromised by hardware/software variations acros…