2 citations · 3 across the 4 of their papers we have counts for
6 papers · 1 filter
TrustmeWatcher: An Application for Workplace Micro-Sensing and Explainable Well-Being Feedback
Chengyu Yu, Leon Jacopo Costa, Zoja Anžur +6
Workplace sensing studies combine long-running behaviour traces with self-reports, yet the tools that collect those data often sit apart from the interface that returns results. We…
Federated Learning with Profile Mapping under Distribution Shifts and Drifts
Mohan Li, Dario Fenoglio, Martin Gjoreski +1
Federated Learning (FL) enables decentralized model training across clients without sharing raw data, but its performance degrades under real-world data heterogeneity. Existing met…
Federated Concept-Based Models: Interpretable models with distributed supervision
Dario Fenoglio, Arianna Casanova, Francesco De Santis +6
Concept-based Models (CMs) enhance interpretability in deep learning by grounding predictions in human-understandable concepts. However, concept annotations are costly and rarely a…
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…
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…