5 papers
ECGXtract: Deep Learning-based ECG Feature Extraction for Automated CVD Diagnosis
Youssif Abuzied, Hassan AbdEltawab, Abdelrhman Gaber +1
This paper presents ECGXtract, a deep learning-based approach for interpretable ECG feature extraction, addressing the limitations of traditional signal processing and black-box ma…
FedLLM-Align: Feature Extraction From Heterogeneous Clients
Abdelrhman Gaber, Muhammad ElMahdy, Youssif Abuzied +2
Federated learning (FL) enables collaborative model training without sharing raw data, making it attractive for privacy-sensitive domains, e.g., healthcare, finance, and IoT. A maj…
FedCVD++: Communication-Efficient Federated Learning for Cardiovascular Risk Prediction with Parametric and Non-Parametric Model Optimization
Abdelrhman Gaber, Hassan Abd-Eltawab, John Elgallab +5
Cardiovascular diseases (CVD) cause over 17 million deaths annually worldwide, highlighting the urgent need for privacy-preserving predictive systems. We introduce FedCVD++, an enh…
MIRA: A Method of Federated MultI-Task Learning for LaRge LAnguage Models
Ahmed Elbakary, Chaouki Ben Issaid, Tamer ElBatt +2
In this paper, we introduce a method for fine-tuning Large Language Models (LLMs), inspired by Multi-Task learning in a federated manner. Our approach leverages the structure of ea…
Fed-Sophia: A Communication-Efficient Second-Order Federated Learning Algorithm
Ahmed Elbakary, Chaouki Ben Issaid, Mohammad Shehab +3
Federated learning is a machine learning approach where multiple devices collaboratively learn with the help of a parameter server by sharing only their local updates. While gradie…