1 citations · 2 across the 6 of their papers we have counts for
6 papers
Sparse Subspace-to-Expert Sharing for Task-Agnostic Continual Learning
Fatema Siddika, Md Anwar Hossen, Tanwi Mallick +1
Continual learning in Large Language Models (LLMs) is hindered by the plasticity-stability dilemma, where acquiring new capabilities often leads to catastrophic forgetting of previ…
CRAFT: Forgetting-Aware Intervention-Based Adaptation for Continual Learning
Md Anwar Hossen, Fatema Siddika, Juan Pablo Munoz +2
Large language models (LLMs) can learn new tasks through fine-tuning, but learning tasks sequentially often leads to catastrophic forgetting of earlier ones. This paper proposes CR…
Sparse Subspace-to-Expert Sharing for Task-Agnostic Continual Learning
Fatema Siddika, Md Anwar Hossen, Tanwi Mallick +1
Continual learning in Large Language Models (LLMs) is hindered by the plasticity-stability dilemma, where acquiring new capabilities often leads to catastrophic forgetting of previ…
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…
Fair Allocation of Bandwidth At Edge Servers For Concurrent Hierarchical Federated Learning
Md Anwar Hossen, Fatema Siddika, Wensheng Zhang
This paper explores concurrent FL processes within a three-tier system, with edge servers between edge devices and FL servers. A challenge in this setup is the limited bandwidth fr…