4 papers
FedEFC: Federated Learning Using Enhanced Forward Correction Against Noisy Labels
Seunghun Yu, Jin-Hyun Ahn, Joonhyuk Kang
Federated Learning (FL) is a powerful framework for privacy-preserving distributed learning. It enables multiple clients to collaboratively train a global model without sharing raw…
GeFL: Model-Agnostic Federated Learning with Generative Models
Honggu Kang, Seohyeon Cha, Joonhyuk Kang
Federated learning (FL) is a distributed training paradigm that enables collaborative learning across clients without sharing local data, thereby preserving privacy. However, the i…
FedSplitX: Federated Split Learning for Computationally-Constrained Heterogeneous Clients
Jiyun Shin, Jinhyun Ahn, Honggu Kang +1
Foundation models (FMs) have demonstrated remarkable performance in machine learning but demand extensive training data and computational resources. Federated learning (FL) address…
On the Temperature of Bayesian Graph Neural Networks for Conformal Prediction
Seohyeon Cha, Honggu Kang, Joonhyuk Kang
Accurate uncertainty quantification in graph neural networks (GNNs) is essential, especially in high-stakes domains where GNNs are frequently employed. Conformal prediction (CP) of…