6 citations · 9 across the 4 of their papers we have counts for
6 papers
Device-Induced Mobility Heterogeneity and Its Impact on Federated Next-Location Prediction
André Felipe Zanella, Paula Delgado de Santos, José Suárez-Varela +3
Predicting human mobility is central to network management, urban planning, and location-based services, yet privacy concerns are increasingly pushing such tasks toward federated l…
Distributed Learning as a Service: The Developer's Perspective
Tianyue Chu, Filippo Vannella, Dimitra Tsigkari +5
Application developers of distributed learning services face challenges that a typical federated learning loop does not address. Specifically, the model updates can still leak priv…
Data Heterogeneity and Forgotten Labels in Split Federated Learning
Joana Tirana, Dimitra Tsigkari, David Solans Noguero +1
In Split Federated Learning (SFL), the clients collaboratively train a model with the help of a server by splitting the model into two parts. Part-1 is trained locally at each clie…
PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning
Luca Corbucci, Mikko A Heikkila, David Solans Noguero +2
Training and deploying Machine Learning models that simultaneously adhere to principles of fairness and privacy while ensuring good utility poses a significant challenge. The inter…
Speech Robust Bench: A Robustness Benchmark For Speech Recognition
Muhammad A. Shah, David Solans Noguero, Mikko A. Heikkila +2
As Automatic Speech Recognition (ASR) models become ever more pervasive, it is important to ensure that they make reliable predictions under corruptions present in the physical and…
Analyzing and Mitigating Bias for Vulnerable Classes: Towards Balanced Representation in Dataset
Dewant Katare, David Solans Noguero, Souneil Park +3
The accuracy and fairness of perception systems in autonomous driving are essential, especially for vulnerable road users such as cyclists, pedestrians, and motorcyclists who face…