4 papers
Energy-Efficient Quantized Federated Learning for Resource-constrained IoT devices
Wilfrid Sougrinoma Compaoré, Yaya Etiabi, El Mehdi Amhoud +1
Federated Learning (FL) has emerged as a promising paradigm for enabling collaborative machine learning while preserving data privacy, making it particularly suitable for Internet…
MetaGraphLoc: A Graph-based Meta-learning Scheme for Indoor Localization via Sensor Fusion
Yaya Etiabi, Eslam Eldeeb, Mohammad Shehab +4
Accurate indoor localization remains challenging due to variations in wireless signal environments and limited data availability. This paper introduces MetaGraphLoc, a novel system…
FeMLoc: Federated Meta-learning for Adaptive Wireless Indoor Localization Tasks in IoT Networks
Yaya Etiabi, Wafa Njima, El Mehdi Amhoud
The rapid growth of the Internet of Things fosters collaboration among connected devices for tasks like indoor localization. However, existing indoor localization solutions struggl…
A Unified Deep Transfer Learning Model for Accurate IoT Localization in Diverse Environments
Abdullahi Isa Ahmed, Yaya Etiabi, Ali Waqar Azim +1
Internet of Things (IoT) is an ever-evolving technological paradigm that is reshaping industries and societies globally. Real-time data collection, analysis, and decision-making fa…