3 papers
cs.LG2025
Progressive Size-Adaptive Federated Learning: A Comprehensive Framework for Heterogeneous Multi-Modal Data Systems
Sajid Hussain, Muhammad Sohail, Nauman Ali Khan +2
Federated Learning (FL) has emerged as a transformative paradigm for distributed machine learning while preserving data privacy. However, existing approaches predominantly focus on…
cs.CR2025
QA-HFL: Quality-Aware Hierarchical Federated Learning for Resource-Constrained Mobile Devices with Heterogeneous Image Quality
Sajid Hussain, Muhammad Sohail, Nauman Ali Khan
This paper introduces QA-HFL, a quality-aware hierarchical federated learning framework that efficiently handles heterogeneous image quality across resource-constrained mobile devi…
cs.CL2025
SEMFED: Semantic-Aware Resource-Efficient Federated Learning for Heterogeneous NLP Tasks
Sajid Hussain, Muhammad Sohail, Nauman Ali Khan
Background: Federated Learning (FL) has emerged as a promising paradigm for training machine learning models while preserving data privacy. However, applying FL to Natural Language…