3 citations · 8 across the 10 of their papers we have counts for
10 papers
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…
Online Bayesian Imbalanced Learning with Bregman-Calibrated Deep Networks
Zahir Alsulaimawi
Class imbalance remains a fundamental challenge in machine learning, where standard classifiers exhibit severe performance degradation in minority classes. Although existing approa…
One-Shot Federated Ridge Regression: Exact Recovery via Sufficient Statistic Aggregation
Zahir Alsulaimawi
Federated learning protocols require repeated synchronization between clients and a central server, with convergence rates depending on learning rates, data heterogeneity, and clie…
Feedback-Enhanced Hallucination-Resistant Vision-Language Model for Real-Time Scene Understanding
Zahir Alsulaimawi
Real-time scene comprehension is a key advance in artificial intelligence, enhancing robotics, surveillance, and assistive tools. However, hallucination remains a challenge. AI sys…
Meta-FL: A Novel Meta-Learning Framework for Optimizing Heterogeneous Model Aggregation in Federated Learning
Zahir Alsulaimawi
Federated Learning (FL) enables collaborative model training across diverse entities while safeguarding data privacy. However, FL faces challenges such as data heterogeneity and mo…
Enhanced Robustness in Wireless Communications through Unified Sequency-Frequency Multiplexing
Zahir Alsulaimawi
In the evolving wireless communications landscape, addressing the challenges of multipath fading and high mobility remains paramount. This paper introduces the Unified Sequency-Fre…
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…