6 citations · 12 across the 6 of their papers we have counts for
6 papers
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…
PSI-PFL: Population Stability Index for Client Selection in non-IID Personalized Federated Learning
Daniel-M. Jimenez-Gutierrez, David Solans, Mohammed Elbamby +1
Federated Learning (FL) enables decentralized machine learning (ML) model training while preserving data privacy by keeping data localized across clients. However, non-independent…
Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions
Daniel M. Jimenez G., David Solans, Mikko Heikkila +4
Recent advances in machine learning have highlighted Federated Learning (FL) as a promising approach that enables multiple distributed users (so-called clients) to collectively tra…
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…