3 papers
cs.LG2024
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…
eess.AS2024
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…
cs.LG2024
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…