3 papers
cs.LG2026
FedEMA-Distill: Exponential Moving Average Guided Knowledge Distillation for Robust Federated Learning
Hamza Reguieg, Mohamed El Kamili, Essaid Sabir
Federated learning (FL) often degrades when clients hold heterogeneous non-Independent and Identically Distributed (non-IID) data and when some clients behave adversarially, leadin…
cs.LG2024
Efficient Collaborations through Weight-Driven Coalition Dynamics in Federated Learning Systems
Mohammed El Hanjri, Hamza Reguieg, Adil Attiaoui +3
In the era of the Internet of Things (IoT), decentralized paradigms for machine learning are gaining prominence. In this paper, we introduce a federated learning model that capital…
cs.LG2023
A Comparative Evaluation of FedAvg and Per-FedAvg Algorithms for Dirichlet Distributed Heterogeneous Data
Hamza Reguieg, Mohammed El Hanjri, Mohamed El Kamili +1
In this paper, we investigate Federated Learning (FL), a paradigm of machine learning that allows for decentralized model training on devices without sharing raw data, there by pre…