3 citations · 3 across the 3 of their papers we have counts for
4 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…