14 papers
When Clients Are Orchestrated: Strategic Gradient Manipulation to Defeat Federated Learning Servers with Efficient Defense
Mohamed Shaaban, Ahmed Abdelnaby, Mohamed Elmahallawy
Federated Learning enables decentralized model training by exchanging model updates--rather than raw data--with a central parameter server (PS). While most of the existing defenses…
Beyond Small Patches: Black-Box Detection and Purification of Diverse Backdoor Triggers
Ahmed Abdelnaby, Mohamed Elmahallawy
Deep neural networks (DNNs) are increasingly deployed in real-world vision systems, yet their predictions can be covertly manipulated by backdoor attacks, in which malicious trigge…
Learning What Can Be Picked: Active Reachability Estimation for Efficient Robotic Fruit Harvesting
Nur Afsa Syeda, Mohamed Elmahallawy, Luis Fernando de la Torre +1
Agriculture remains a cornerstone of global health and economic sustainability, yet labor-intensive tasks such as harvesting high-value crops continue to face growing workforce sho…
Prototype Fusion: A Training-Free Multi-Layer Approach to OOD Detection
Shreen Gul, Mohamed Elmahallawy, Ardhendu Tripathy +1
Deep learning models are increasingly deployed in safety-critical applications, where reliable out-of-distribution (OOD) detection is essential to ensure robustness. Existing metho…
SecureGate: Learning When to Reveal PII Safely via Token-Gated Dual-Adapters for Federated LLMs
Mohamed Shaaban, Mohamed Elmahallawy
Federated learning (FL) enables collaborative training across organizational silos without sharing raw data, making it attractive for privacy-sensitive applications. With the rapid…
Explaining the Unseen: Multimodal Vision-Language Reasoning for Situational Awareness in Underground Mining Disasters
Mizanur Rahman Jewel, Mohamed Elmahallawy, Sanjay Madria +1
Underground mining disasters produce pervasive darkness, dust, and collapses that obscure vision and make situational awareness difficult for humans and conventional systems. To ad…