47 citations · 127 across the 10 of their papers we have counts for
11 papers
Fed-ComBat: A Generalized Federated Framework for Batch Effect Harmonization in Collaborative Studies
Santiago Silva, Ghiles Reguig, Neil P Oxtoby +2
The use of multi-centric analyses is crucial for obtaining sufficient sample sizes and representative clinical populations in experimental studies. In this setting, data harmonizat…
On Tail Decay Rate Estimation of Loss Function Distributions
Etrit Haxholli, Marco Lorenzi
The study of loss function distributions is critical to characterize a model's behaviour on a given machine learning problem. For example, while the quality of a model is commonly…
Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications
Francesco Cremonesi, Marc Vesin, Sergen Cansiz +16
The real-world implementation of federated learning is complex and requires research and development actions at the crossroad between different domains ranging from data science, t…
Fed-MIWAE: Federated Imputation of Incomplete Data via Deep Generative Models
Irene Balelli, Aude Sportisse, Francesco Cremonesi +2
Federated learning allows for the training of machine learning models on multiple decentralized local datasets without requiring explicit data exchange. However, data pre-processin…
SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization
Yann Fraboni, Martin Van Waerebeke, Kevin Scaman +3
Machine Unlearning (MU) is an increasingly important topic in machine learning safety, aiming at removing the contribution of a given data point from a training procedure. Federate…
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers +21
Federated Learning (FL) is a novel approach enabling several clients holding sensitive data to collaboratively train machine learning models, without centralizing data. The cross-s…
A General Theory for Federated Optimization with Asynchronous and Heterogeneous Clients Updates
Yann Fraboni, Richard Vidal, Laetitia Kameni +1
We propose a novel framework to study asynchronous federated learning optimization with delays in gradient updates. Our theoretical framework extends the standard FedAvg aggregatio…
A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations
Irene Balelli, Santiago Silva, Marco Lorenzi
We propose a novel federated learning paradigm to model data variability among heterogeneous clients in multi-centric studies. Our method is expressed through a hierarchical Bayesi…
A General Theory for Client Sampling in Federated Learning
Yann Fraboni, Richard Vidal, Laetitia Kameni +1
While client sampling is a central operation of current state-of-the-art federated learning (FL) approaches, the impact of this procedure on the convergence and speed of FL remains…
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning
Yann Fraboni, Richard Vidal, Laetitia Kameni +1
This work addresses the problem of optimizing communications between server and clients in federated learning (FL). Current sampling approaches in FL are either biased, or non opti…
Free-rider Attacks on Model Aggregation in Federated Learning
Yann Fraboni, Richard Vidal, Marco Lorenzi
Free-rider attacks against federated learning consist in dissimulating participation to the federated learning process with the goal of obtaining the final aggregated model without…