20 citations · 20 across the 4 of their papers we have counts for
4 papers
MineGrad: Gradient Inversion Attacks on LoRA Fine-Tuning
Hasin Us Sami, Swapneel Sen, Basak Guler
Parameter-efficient fine-tuning (PEFT), such as low-rank adaptation (LoRA), has recently been adopted in federated learning to reduce communication and computation costs. In this s…
ProtAlign: Contrastive learning paradigm for Sequence and structure alignment
Aditya Ranganath, Hasin Us Sami, Kowshik Thopalli +2
Protein language models often take into consideration the alignment between a protein sequence and its textual description. However, they do not take structural information into co…
Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning
Hasin Us Sami, Swapneel Sen, Amit K. Roy-Chowdhury +2
Federated learning (FL) allows multiple data-owners to collaboratively train machine learning models by exchanging local gradients, while keeping their private data on-device. To s…
Sparsified Secure Aggregation for Privacy-Preserving Federated Learning
Irem Ergun, Hasin Us Sami, Basak Guler
Secure aggregation is a popular protocol in privacy-preserving federated learning, which allows model aggregation without revealing the individual models in the clear. On the other…