2 citations · 3 across the 4 of their papers we have counts for
4 papers
Probing the Efficacy of Federated Parameter-Efficient Fine-Tuning of Vision Transformers for Medical Image Classification
Naif Alkhunaizi, Faris Almalik, Rouqaiah Al-Refai +2
With the advent of large pre-trained transformer models, fine-tuning these models for various downstream tasks is a critical problem. Paucity of training data, the existence of dat…
FedSIS: Federated Split Learning with Intermediate Representation Sampling for Privacy-preserving Generalized Face Presentation Attack Detection
Naif Alkhunaizi, Koushik Srivatsan, Faris Almalik +2
Lack of generalization to unseen domains/attacks is the Achilles heel of most face presentation attack detection (FacePAD) algorithms. Existing attempts to enhance the generalizabi…
FeSViBS: Federated Split Learning of Vision Transformer with Block Sampling
Faris Almalik, Naif Alkhunaizi, Ibrahim Almakky +1
Data scarcity is a significant obstacle hindering the learning of powerful machine learning models in critical healthcare applications. Data-sharing mechanisms among multiple entit…
Suppressing Poisoning Attacks on Federated Learning for Medical Imaging
Naif Alkhunaizi, Dmitry Kamzolov, Martin Takáč +1
Collaboration among multiple data-owning entities (e.g., hospitals) can accelerate the training process and yield better machine learning models due to the availability and diversi…