7 citations · 8 across the 3 of their papers we have counts for
3 papers
cs.LG2024
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…
cs.LG2024★ 1 cited
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…
cs.LG2024★ 7 cited
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…