Research Projects
Selected research highlights and publications
Research Highlights
SLADE: Shielding against Dual Exploits in Large Vision-Language Models
Research Architecture Overview: Our novel defense mechanism protects Vision-Language Models against dual exploits through robust encoding and adversarial training techniques.
When Data is Scarce, Learn to Adapt: Robust Federated Learning via Adversarial Meta-Optimization
Research Architecture Overview: We propose FAML, the first and robust FAT framework that leverages meta-learning to enhance robustness in federated learning and address the challenges posed by data scarcity in heterogeneous clients.
Towards Trustworthy Autonomous Vehicles with Vision-Language Models Under Adversarial Attacks
Research Architecture Overview: Examining the robustness of Vision-Language Models in autonomous vehicle applications under targeted and untargeted adversarial attacks.
Sim-CLIP: Unsupervised Siamese Adversarial Fine-Tuning for Robust Vision-Language Models
Research Architecture Overview: Unsupervised approach to enhance Vision-Language Models through Siamese adversarial fine-tuning for improved robustness and semantic richness.
Blockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computing
Research Architecture Overview: Blockchain-enhanced framework ensuring privacy and security in distributed machine learning across edge computing environments.
Research Architecture Overview: Comprehensive defense mechanism for Vision-Language Models focusing on robust encoding techniques against various attack vectors.
TriplePlay: Enhancing Federated Learning with CLIP for Non-IID Data and Resource Efficiency
Research Architecture Overview: TriplePlay, a framework that tailors CLIP foundation model as an adapter to strengthen FL model’s performance and adaptability across heterogeneous data distributions among the clients.
Quantifying Robustness and Sustainability Trade-off in Federated Adversarial Learning for Cyber-Physical Systems
Research Architecture Overview: Federated Adversarial Learning simulation with nine heterogeneous devices with the different configurations.
Research Architecture Overview: Securing Privacy in Cloud-Based Whiteboard Services Against Health Attribute Inference Attacks.
Research Architecture Overview:
Research Architecture Overview: Overview of experimental process: learning from hand drawing dataset, real-time drawings from DP-WhiteBoard tool, and recognizing shape and inference via transfer learning models.
