7 citations · 20 across the 9 of their papers we have counts for
3 papers · 1 filter
Federated Learning Client Pruning for Noisy Labels
Mahdi Morafah, Hojin Chang, Chen Chen +1
Federated Learning (FL) enables collaborative model training across decentralized edge devices while preserving data privacy. However, existing FL methods often assume clean annota…
Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integration
Mahdi Morafah, Vyacheslav Kungurtsev, Hojin Chang +2
Federated Learning has emerged as a promising paradigm for collaborative machine learning, while preserving user data privacy. Despite its potential, standard FL lacks support for…
Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Data
Mahdi Morafah, Matthias Reisser, Bill Lin +1
The proliferation of edge devices has brought Federated Learning (FL) to the forefront as a promising paradigm for decentralized and collaborative model training while preserving t…