46 citations · 109 across the 34 of their papers we have counts for
6 papers · 1 filter
RAG-HAR+: Towards Cost-Efficient LLM-Based Human Activity Recognition for Edge Deployment
Hansi Karunarathna, Nirhoshan Sivaroopan, Chamara Madarasingha +2
Human Activity Recognition (HAR) from wearable sensors supports applications in healthcare, rehabilitation, fitness tracking, and smart environments. Yet, existing deep learning ap…
STELLA: Efficient Sensor-to-LLM Translation for On-Device Human Activity Recognition
Nirhoshan Sivaroopan, Albert Zomaya, Kanchana Thilakarathna
HAR is increasingly expected to run continuously on edge devices, yet recent LLM-based methods remain hard to deploy: raw sensor prompts are long, cloud inference adds latency and…
Memory Retrieval in Transformers: Insights from The Encoding Specificity Principle
Viet Hung Dinh, Ming Ding, Youyang Qu +1
While explainable artificial intelligence (XAI) for large language models (LLMs) remains an evolving field with many unresolved questions, increasing regulatory pressures have spur…
Personalizing Federated Learning for Hierarchical Edge Networks with Non-IID Data
Seunghyun Lee, Omid Tavallaie, Shuaijun Chen +4
Accommodating edge networks between IoT devices and the cloud server in Hierarchical Federated Learning (HFL) enhances communication efficiency without compromising data privacy. H…
SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks
Omid Tavallaie, Kanchana Thilakarathna, Suranga Seneviratne +2
Federated Learning (FL) is a distributed machine learning paradigm designed for privacy-sensitive applications that run on resource-constrained devices with non-Identically and Ind…
CAFe: Cost and Age aware Federated Learning
Sahan Liyanaarachchi, Kanchana Thilakarathna, Sennur Ulukus
In many federated learning (FL) models, a common strategy employed to ensure the progress in the training process, is to wait for at least clients out of the total clients…