17 citations · 51 across the 9 of their papers we have counts for
12 papers
Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata
Michael Ben Ali, Imen Megdiche, André Péninou +1
Clustered Federated Learning (CFL) addresses data heterogeneity in federated settings by grouping clients with similar data distributions to enable effective training. Existing met…
A Robust Clustered Federated Learning Approach for Non-IID Data with Quantity Skew
Michael Ben Ali, Imen Megdiche, André Peninou +1
Federated Learning (FL) is a decentralized paradigm that enables a client-server architecture to collaboratively train a global Artificial Intelligence model without sharing raw da…
A survey on Clustered Federated Learning: Taxonomy, Analysis and Applications
Michael Ben Ali, Omar El-Rifai, Imen Megdiche +2
As Federated Learning (FL) expands, the challenge of non-independent and identically distributed (non-IID) data becomes critical. Clustered Federated Learning (CFL) addresses this…
Comparative Evaluation of Clustered Federated Learning Methods
Michael Ben Ali, Omar El-Rifai, Imen Megdiche +2
Over recent years, Federated Learning (FL) has proven to be one of the most promising methods of distributed learning which preserves data privacy. As the method evolved and was co…
DECWA : Density-Based Clustering using Wasserstein Distance
Nabil El Malki, Robin Cugny, Olivier Teste +1
Clustering is a data analysis method for extracting knowledge by discovering groups of data called clusters. Among these methods, state-of-the-art density-based clustering methods…
Why Should I Choose You? AutoXAI: A Framework for Selecting and Tuning eXplainable AI Solutions
Robin Cugny, Julien Aligon, Max Chevalier +2
In recent years, a large number of XAI (eXplainable Artificial Intelligence) solutions have been proposed to explain existing ML (Machine Learning) models or to create interpretabl…