3 citations · 8 across the 10 of their papers we have counts for
5 papers · 1 filter
Distributed Deep Variational Approach for Privacy-preserving Data Release
Zahir Alsulaimawi, Huaping Liu
Federated learning (FL) lets distributed nodes train a shared model without exchanging their raw data, but in privacy-sensitive deployments medical sensors, IoT devices, wearables…
Synergizing Privacy and Utility in Data Analytics Through Advanced Information Theorization
Zahir Alsulaimawi
This study develops a novel framework for privacy-preserving data analytics, addressing the critical challenge of balancing data utility with privacy concerns. We introduce three s…
Securing Federated Learning with Control-Flow Attestation: A Novel Framework for Enhanced Integrity and Resilience against Adversarial Attacks
Zahir Alsulaimawi
The advent of Federated Learning (FL) as a distributed machine learning paradigm has introduced new cybersecurity challenges, notably adversarial attacks that threaten model integr…
Federated Learning with Anomaly Detection via Gradient and Reconstruction Analysis
Zahir Alsulaimawi
In the evolving landscape of Federated Learning (FL), the challenge of ensuring data integrity against poisoning attacks is paramount, particularly for applications demanding strin…
Enhancing Security in Federated Learning through Adaptive Consensus-Based Model Update Validation
Zahir Alsulaimawi
This paper introduces an advanced approach for fortifying Federated Learning (FL) systems against label-flipping attacks. We propose a simplified consensus-based verification proce…