15 citations · 40 across the 11 of their papers we have counts for
11 papers
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…
A Feature-Driven Framework for Software Fault Prediction
Ahmad Nauman Ghazi, Nagajyothi Devarapalli, Ashir Javeed +3
Software fault prediction (SFP) is a critical task in software engineering, enabling early identification of faults in modules to improve software quality and reduce maintenance co…
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…
From Requirements to Test Cases: An NLP-Based Approach for High-Performance ECU Test Case Automation
Nikitha Medeshetty, Ahmad Nauman Ghazi, Sadi Alawadi +1
Automating test case specification generation is vital for improving the efficiency and accuracy of software testing, particularly in complex systems like high-performance Electron…
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,…
FedCSD: A Federated Learning Based Approach for Code-Smell Detection
Sadi Alawadi, Khalid Alkharabsheh, Fahed Alkhabbas +4
This paper proposes a Federated Learning Code Smell Detection (FedCSD) approach that allows organizations to collaboratively train federated ML models while preserving their data p…
FedBot: Enhancing Privacy in Chatbots with Federated Learning
Addi Ait-Mlouk, Sadi Alawadi, Salman Toor +1
Chatbots are mainly data-driven and usually based on utterances that might be sensitive. However, training deep learning models on shared data can violate user privacy. Such issues…
FedQAS: Privacy-aware machine reading comprehension with federated learning
Addi Ait-Mlouk, Sadi Alawadi, Salman Toor +1
Machine reading comprehension (MRC) of text data is one important task in Natural Language Understanding. It is a complex NLP problem with a lot of ongoing research fueled by the r…
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…