5 papers
MI-to-Mid Distilled Compression (M2M-DC): An Hybrid-Information-Guided-Block Pruning with Progressive Inner Slicing Approach to Model Compression
Lionel Levine, Haniyeh Ehsani Oskouie, Sajjad Ghiasvand +1
We introduce MI-to-Mid Distilled Compression (M2M-DC), a two-scale, shape-safe compression framework that interleaves information-guided block pruning with progressive inner slicin…
pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language Models
Sajjad Ghiasvand, Mahnoosh Alizadeh, Ramtin Pedarsani
Vision-Language Models (VLMs) like CLIP have demonstrated remarkable generalization in zero- and few-shot settings, but adapting them efficiently to decentralized, heterogeneous da…
Few-Shot Adversarial Low-Rank Fine-Tuning of Vision-Language Models
Sajjad Ghiasvand, Haniyeh Ehsani Oskouie, Mahnoosh Alizadeh +1
Vision-Language Models (VLMs) such as CLIP have shown remarkable performance in cross-modal tasks through large-scale contrastive pre-training. To adapt these large transformer-bas…
Decentralized Low-Rank Fine-Tuning of Large Language Models
Sajjad Ghiasvand, Mahnoosh Alizadeh, Ramtin Pedarsani
While parameter-efficient fine-tuning (PEFT) techniques like Low-Rank Adaptation (LoRA) offer computationally efficient adaptations of Large Language Models (LLMs), their practical…
Communication-Efficient and Tensorized Federated Fine-Tuning of Large Language Models
Sajjad Ghiasvand, Yifan Yang, Zhiyu Xue +3
Parameter-efficient fine-tuning (PEFT) methods typically assume that Large Language Models (LLMs) are trained on data from a single device or client. However, real-world scenarios…