2 citations · 3 across the 3 of their papers we have counts for
5 papers
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…
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…
Counterfactual Explanations for Clustering Models
Aurora Spagnol, Kacper Sokol, Pietro Barbiero +2
Clustering algorithms rely on complex optimisation processes that may be difficult to comprehend, especially for individuals who lack technical expertise. While many explainable ar…