Author

Mohamed Elmahallawy

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#machine learning Preprint Sep 2026

Resource-Aware Federated Mixture-of-Experts with Adaptive Pruning for Onboard Learning in LEO Satellite Constellations

Low-Earth-orbit (LEO) satellites are increasingly expected to perform onboard learning for applications such as disaster response and environmental monitoring. However, conventional federated learning (FL) is ill-suited to onboard satellite learning, as it assumes computational, memory, and communication resources beyo...

M. Shaaban, Mohamed Elmahallawy, Marius Bernahrndt et al. · 0 citations

#small language model Conference Open access Sep 2026

Region-Level Black-Box Defense Against Stealthy Embedding-Space Backdoors in CLIP

CLIPGuard is proposed, a lightweight and fully black-box defense specifically designed to mitigate embedding-space backdoors in CLIP encoders, consistently outperforming existing black-box defenses, including CleanCLIP and CleanerCLIP.

A. Abdel-Naby, Mohamed Elmahallawy · Pacific-Asia Conference on K... · 1 citation

#artificial intelligence Preprint Sep 2026

When Clients Are Orchestrated: Strategic Gradient Manipulation to Defeat Federated Learning Servers with Efficient Defense

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 primarily assume static or independently acting adversaries, we reveal a new class of dynamically adaptive attacks that systematically bypa...

M. Shaaban, A. Abdel-Naby, Mohamed Elmahallawy · 0 citations

Preprint Sep 2026

Beyond Small Patches: Black-Box Detection and Purification of Diverse Backdoor Triggers

TRIM (Trigger Removal by Identifying Manipulated Regions), a deployment-oriented black-box defense that detects and selectively removes backdoor triggers at inference time without requiring model internals, training data, or clean samples, is proposed.

A. Abdel-Naby, Mohamed Elmahallawy · 0 citations