6 papers
FedADAS: Communication-Efficient Federated Distillation for On-Device Driver Yawn Recognition in Vehicular Networks
Ahmed Mujtaba, Gleb Radchenko, Marc Masana +1
Driver fatigue is a critical safety concern in advanced driver assistance systems. Driver monitoring models trained off-site on static datasets adapt poorly to real-world condition…
YawDD+: Frame-level Annotations for Accurate Yawn Prediction
Ahmed Mujtaba, Gleb Radchenko, Marc Masana +1
Driver fatigue remains a leading cause of road accidents, responsible for 24% of crashes. While yawning serves as an early behavioral indicator of fatigue, existing approaches face…
Federated Distillation on Edge Devices: Efficient Client-Side Filtering for Non-IID Data
Ahmed Mujtaba, Gleb Radchenko, Radu Prodan +1
Federated distillation has emerged as a promising collaborative machine learning approach, offering enhanced privacy protection and reduced communication compared to traditional fe…
Edge AI Collaborative Learning: Bayesian Approaches to Uncertainty Estimation
Gleb Radchenko, Victoria Andrea Fill
Recent advancements in edge computing have significantly enhanced the AI capabilities of Internet of Things (IoT) devices. However, these advancements introduce new challenges in k…
Uncertainty Estimation in Multi-Agent Distributed Learning for AI-Enabled Edge Devices
Gleb Radchenko, Victoria Andrea Fill
Initially considered as low-power units with limited autonomous processing, Edge IoT devices have seen a paradigm shift with the introduction of FPGAs and AI accelerators. This adv…
Uncertainty Estimation in Multi-Agent Distributed Learning
Gleb Radchenko, Victoria Andrea Fill
Traditionally, IoT edge devices have been perceived primarily as low-power components with limited capabilities for autonomous operations. Yet, with emerging advancements in embedd…