2 citations · 2 across the 7 of their papers we have counts for
3 papers · 1 filter
GC-Fed: Gradient Centralized Federated Learning with Partial Client Participation
Jungwon Seo, Ferhat Ozgur Catak, Chunming Rong +2
Federated Learning (FL) enables privacy-preserving multi-source information fusion (MSIF) but is challenged by client drift in highly heterogeneous data settings. Many existing dri…
Understanding Federated Learning from IID to Non-IID dataset: An Experimental Study
Jungwon Seo, Ferhat Ozgur Catak, Chunming Rong
As privacy concerns and data regulations grow, federated learning (FL) has emerged as a promising approach for training machine learning models across decentralized data sources wi…
FedShift: Robust Federated Learning Aggregation Scheme in Resource Constrained Environment via Weight Shifting
Jungwon Seo, Minhoe Kim, Chunming Rong
Federated Learning (FL) commonly relies on a central server to coordinate training across distributed clients. While effective, this paradigm suffers from significant communication…