FedSplitX: Federated Split Learning for Computationally-Constrained Heterogeneous Clients

no code implementations • 23 Oct 2023 • Jiyun Shin, JinHyun Ahn, Honggu Kang, Joonhyuk Kang

Foundation models (FMs) have demonstrated remarkable performance in machine learning but demand extensive training data and computational resources.

Federated Learning

On the Temperature of Bayesian Graph Neural Networks for Conformal Prediction

no code implementations • 17 Oct 2023 • Seohyeon Cha, Honggu Kang, Joonhyuk Kang

Building on a recent work that introduced a scaling parameter for constructing valid credible regions from posterior estimate, our study explores the advantages of incorporating a temperature parameter into Bayesian GNNs within CP framework.

Conformal Prediction Uncertainty Quantification +1

NeFL: Nested Federated Learning for Heterogeneous Clients

no code implementations • 15 Aug 2023 • Honggu Kang, Seohyeon Cha, Jinwoo Shin, Jongmyeong Lee, Joonhyuk Kang

Previous studies tackle the system heterogeneity by splitting a model into submodels, but with less degree-of-freedom in terms of model architecture.

Federated Learning