7 citations · 24 across the 18 of their papers we have counts for
19 papers
Semantics-Aware Hierarchical Token Communication: Clustering, Bit Mapping, and Power Allocation
Jihoon Lee, Seungeun Oh, Jihong Park +2
Despite the rise of token communication (TokCom) as a new paradigm beyond traditional bit communication, existing approaches have primarily adopted artificial intelligence (AI)-cen…
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…
Action Deviation-Aware Inference for Low-Latency Wireless Robots
Jeyoung Park, Yeonsub Lim, Seungeun Oh +3
To support latency-sensitive AI applications ranging from autonomous driving to industrial robot manipulation, 6G envisions distributed ML with computational resources in mobile, e…
Deadline-Aware Bandwidth Allocation for Semantic Generative Communication with Diffusion Models
Jinhyuk Choi, Jihong Park, Seungeun Oh +1
The importance of Radio Access Network (RAN) in support Artificial Intelligence (AI) application services has grown significantly, underscoring the need for an integrated approach…
Energy-Efficient Wireless LLM Inference via Uncertainty and Importance-Aware Speculative Decoding
Jihoon Park, Seungeun Oh, Seong-Lyun Kim
To address the growing demand for on-device LLM inference in resource-constrained environments, hybrid language models (HLM) have emerged, combining lightweight local models with p…
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-…
Uncertainty-Aware Hybrid Inference with On-Device Small and Remote Large Language Models
Seungeun Oh, Jinhyuk Kim, Jihong Park +3
This paper studies a hybrid language model (HLM) architecture that integrates a small language model (SLM) operating on a mobile device with a large language model (LLM) hosted at…
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…
Knowledge Distillation from Language-Oriented to Emergent Communication for Multi-Agent Remote Control
Yongjun Kim, Sejin Seo, Jihong Park +3
In this work, we compare emergent communication (EC) built upon multi-agent deep reinforcement learning (MADRL) and language-oriented semantic communication (LSC) empowered by a pr…
Generative AI Meets Semantic Communication: Evolution and Revolution of Communication Tasks
Eleonora Grassucci, Jihong Park, Sergio Barbarossa +3
While deep generative models are showing exciting abilities in computer vision and natural language processing, their adoption in communication frameworks is still far underestimat…
Towards Semantic Communication Protocols for 6G: From Protocol Learning to Language-Oriented Approaches
Jihong Park, Seung-Woo Ko, Jinho Choi +2
The forthcoming 6G systems are expected to address a wide range of non-stationary tasks. This poses challenges to traditional medium access control (MAC) protocols that are static…
Semantics Alignment via Split Learning for Resilient Multi-User Semantic Communication
Jinhyuk Choi, Jihong Park, Seung-Woo Ko +3
Recent studies on semantic communication commonly rely on neural network (NN) based transceivers such as deep joint source and channel coding (DeepJSCC). Unlike traditional transce…
Language-Oriented Communication with Semantic Coding and Knowledge Distillation for Text-to-Image Generation
Hyelin Nam, Jihong Park, Jinho Choi +2
By integrating recent advances in large language models (LLMs) and generative models into the emerging semantic communication (SC) paradigm, in this article we put forward to a nov…
Sequential Semantic Generative Communication for Progressive Text-to-Image Generation
Hyelin Nam, Jihong Park, Jinho Choi +1
This paper proposes new framework of communication system leveraging promising generation capabilities of multi-modal generative models. Regarding nowadays smart applications, succ…
SplitAMC: Split Learning for Robust Automatic Modulation Classification
Jihoon Park, Seungeun Oh, Seong-Lyun Kim
Automatic modulation classification (AMC) is a technology that identifies a modulation scheme without prior signal information and plays a vital role in various applications, inclu…
Enabling the Wireless Metaverse via Semantic Multiverse Communication
Jihong Park, Jinho Choi, Seong-Lyun Kim +1
Metaverse over wireless networks is an emerging use case of the sixth generation (6G) wireless systems, posing unprecedented challenges in terms of its multi-modal data transmissio…
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…
Towards Semantic Communication Protocols: A Probabilistic Logic Perspective
Sejin Seo, Jihong Park, Seung-Woo Ko +3
Classical medium access control (MAC) protocols are interpretable, yet their task-agnostic control signaling messages (CMs) are ill-suited for emerging mission-critical application…
Visual Transformer Meets CutMix for Improved Accuracy, Communication Efficiency, and Data Privacy in Split Learning
Sihun Baek, Jihong Park, Praneeth Vepakomma +3
This article seeks for a distributed learning solution for the visual transformer (ViT) architectures. Compared to convolutional neural network (CNN) architectures, ViTs often have…