From the 1 of 4 linked papers with an AI index.
4 papers
Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata
Michael Ben Ali, Imen Megdiche, André Péninou +1
The paper introduces FLAMECHE, a method that reformulates metadata‑based clustered federated learning as a distributed Expectation‑Maximization process, allowing additive server up…
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…
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…
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…