15 citations · 40 across the 11 of their papers we have counts for
6 papers · 1 filter
Optimized Federated Knowledge Distillation with Distributed Neural Architecture Search
Chaimaa Medjadji, Sylvain Kubler, Yves Le Traon +3
Federated Learning (FL) enables collaborative model training without centralizing data. However, real-world deployments must simultaneously address statistical heterogeneity across…
FedSparQ: Adaptive Sparse Quantization with Error Feedback for Robust & Efficient Federated Learning
Chaimaa Medjadji, Sadi Alawadi, Feras M. Awaysheh +3
Federated Learning (FL) enables collaborative model training across decentralized clients while preserving data privacy by keeping raw data local. However, FL suffers from signific…
SHEDAD: SNN-Enhanced District Heating Anomaly Detection for Urban Substations
Jonne van Dreven, Abbas Cheddad, Sadi Alawadi +3
District Heating (DH) systems are essential for energy-efficient urban heating. However, despite the advancements in automated fault detection and diagnosis (FDD), DH still faces c…
Beyond Random Noise: Insights on Anonymization Strategies from a Latent Bandit Study
Alexander Galozy, Sadi Alawadi, Victor Kebande +1
This paper investigates the issue of privacy in a learning scenario where users share knowledge for a recommendation task. Our study contributes to the growing body of research on…
Toward efficient resource utilization at edge nodes in federated learning
Sadi Alawadi, Addi Ait-Mlouk, Salman Toor +1
Federated learning (FL) enables edge nodes to collaboratively contribute to constructing a global model without sharing their data. This is accomplished by devices computing local,…
Scalable federated machine learning with FEDn
Morgan Ekmefjord, Addi Ait-Mlouk, Sadi Alawadi +5
Federated machine learning has great promise to overcome the input privacy challenge in machine learning. The appearance of several projects capable of simulating federated learnin…