6 citations · 6 across the 2 of their papers we have counts for
5 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…
Data Lake Ingestion Management
Yan Zhao, Imen Megdiche, Franck Ravat
Data Lake (DL) is a Big Data analysis solution which ingests raw data in their native format and allows users to process these data upon usage. Data ingestion is not a simple copy…