434 citations · 525 across the 14 of their papers we have counts for
4 papers · 1 filter
Breaking the Capacity Bottleneck in Model-Heterogeneous Federated Learning via Gradual Model Restoration
Chengjie Ma, Seungeun Oh, Jihong Park +1
Federated learning (FL) enables distributed model training, yet in heterogeneous deployments, Bandwidth-Constrained Clients (BCCs) often contribute inefficiently due to limited upl…
Communication-Efficient Hybrid Language Model via Uncertainty-Aware Opportunistic and Compressed Transmission
Seungeun Oh, Jinhyuk Kim, Jihong Park +4
To support emerging language-based applications using dispersed and heterogeneous computing resources, the hybrid language model (HLM) offers a promising architecture, where an on-…
Privacy-Preserving Split Learning with Vision Transformers using Patch-Wise Random and Noisy CutMix
Seungeun Oh, Sihun Baek, Jihong Park +5
In computer vision, the vision transformer (ViT) has increasingly superseded the convolutional neural network (CNN) for improved accuracy and robustness. However, ViT's large model…
Differentially Private CutMix for Split Learning with Vision Transformer
Seungeun Oh, Jihong Park, Sihun Baek +5
Recently, vision transformer (ViT) has started to outpace the conventional CNN in computer vision tasks. Considering privacy-preserving distributed learning with ViT, federated lea…