101 citations · 105 across the 19 of their papers we have counts for
5 papers · 1 filter
Multi-Frequency Federated Learning for Human Activity Recognition Using Head-Worn Sensors
Dario Fenoglio, Mohan Li, Davide Casnici +5
Human Activity Recognition (HAR) benefits various application domains, including health and elderly care. Traditional HAR involves constructing pipelines reliant on centralized use…
FLUX: Efficient Descriptor-Driven Clustered Federated Learning under Arbitrary Distribution Shifts
Dario Fenoglio, Mohan Li, Pietro Barbiero +3
Federated Learning (FL) enables collaborative model training across multiple clients while preserving data privacy. Traditional FL methods often use a global model to fit all clien…
Towards Transparent Reasoning: What Drives Faithfulness in Large Language Models?
Teague McMillan, Gabriele Dominici, Martin Gjoreski +1
Large Language Models (LLMs) often produce explanations that do not faithfully reflect the factors driving their predictions. In healthcare settings, such unfaithfulness is especia…
On the interplay of Explainability, Privacy and Predictive Performance with Explanation-assisted Model Extraction
Fatima Ezzeddine, Rinad Akel, Ihab Sbeity +3
Machine Learning as a Service (MLaaS) has gained important attraction as a means for deploying powerful predictive models, offering ease of use that enables organizations to levera…
A Survey on Federated Learning in Human Sensing
Mohan Li, Martin Gjoreski, Pietro Barbiero +4
Human Sensing, a field that leverages technology to monitor human activities, psycho-physiological states, and interactions with the environment, enhances our understanding of huma…