7 papers · 1 filter
CRAFT: Forgetting-Aware Intervention-Based Adaptation for Continual Learning
Md Anwar Hossen, Fatema Siddika, Juan Pablo Munoz +2
Large language models (LLMs) can acquire new capabilities through fine-tuning, but continual adaptation often leads to catastrophic forgetting. We propose CRAFT, a continual learni…
FedReFT: Federated Representation Fine-Tuning with All-But-Me Aggregation
Fatema Siddika, Md Anwar Hossen, J. Pablo Muñoz +3
Parameter-efficient fine-tuning (PEFT) adapts large pre-trained models by updating only a small subset of parameters. Recently, Representation Fine-Tuning (ReFT) has emerged as an…
Dual-Distilled Heterogeneous Federated Learning with Adaptive Margins for Trainable Global Prototypes
Fatema Siddika, Md Anwar Hossen, Wensheng Zhang +3
Heterogeneous Federated Learning (HFL) has gained significant attention for its capacity to handle both model and data heterogeneity across clients. Prototype-based HFL methods eme…
PerfMamba: Performance Analysis and Pruning of Selective State Space Models
Abdullah Al Asif, Mobina Kashaniyan, Sixing Yu +2
Recent advances in sequence modeling have introduced selective SSMs as promising alternatives to Transformer architectures, offering theoretical computational efficiency and sequen…
Federated Multimodal Learning with Dual Adapters and Selective Pruning for Communication and Computational Efficiency
Duy Phuong Nguyen, J. Pablo Munoz, Tanya Roosta +1
Federated Learning (FL) enables collaborative learning across distributed clients while preserving data privacy. However, FL faces significant challenges when dealing with heteroge…
Resource-Aware Heterogeneous Federated Learning using Neural Architecture Search
Sixing Yu, J. Pablo Muñoz, Ali Jannesari
Federated Learning (FL) is extensively used to train AI/ML models in distributed and privacy-preserving settings. Participant edge devices in FL systems typically contain non-indep…