434 citations · 1.3k across the 83 of their papers we have counts for
91 papers
Resilient LLM-Empowered Semantic MAC Protocols via Zero-Shot Adaptation and Knowledge Distillation
Yongjun Kim, Jihong Park, Mehdi Bennis +1
Neural network-based medium access control (MAC) protocol models (NPMs) improve goodput through site-specific operations but are vulnerable to shifts from their training network en…
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In computer vision, the vision transformer (ViT) has increasingly superseded the convolutional neural network (CNN) for improved accuracy and robustness.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…
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…
CSI-Inpainter: Enabling Visual Scene Recovery from CSI Time Sequences for Occlusion Removal
Cheng Chen, Shoki Ohta, Takayuki Nishio +3
Introducing CSI-Inpainter, a pioneering approach for occlusion removal using Channel State Information (CSI) time sequences, this work propels the application of wireless signal pr…
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…
Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Coding
Won Joon Yun, Jae Pyoung Kim, Hankyul Baek +4
While witnessing the noisy intermediate-scale quantum (NISQ) era and beyond, quantum federated learning (QFL) has recently become an emerging field of study. In QFL, each quantum c…
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…
Predictive Closed-Loop Remote Control over Wireless Two-Way Split Koopman Autoencoder
Abanoub M. Girgis, Hyowoon Seo, Jihong Park +2
Real-time remote control over wireless is an important-yet-challenging application in 5G and beyond due to its mission-critical nature under limited communication resources. Curren…
Slimmable Quantum Federated Learning
Won Joon Yun, Jae Pyoung Kim, Soyi Jung +3
Quantum federated learning (QFL) has recently received increasing attention, where quantum neural networks (QNNs) are integrated into federated learning (FL). In contrast to the ex…
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…
Quantum Multi-Agent Reinforcement Learning via Variational Quantum Circuit Design
Won Joon Yun, Yunseok Kwak, Jae Pyoung Kim +4
In recent years, quantum computing (QC) has been getting a lot of attention from industry and academia. Especially, among various QC research topics, variational quantum circuit (V…
SlimFL: Federated Learning with Superposition Coding over Slimmable Neural Networks
Won Joon Yun, Yunseok Kwak, Hankyul Baek +5
Federated learning (FL) is a key enabler for efficient communication and computing, leveraging devices' distributed computing capabilities. However, applying FL in practice is chal…
Attention Based Communication and Control for Multi-UAV Path Planning
Hamid Shiri, Hyowoon Seo, Jihong Park +1
Inspired by the multi-head attention (MHA) mechanism in natural language processing, this letter proposes an iterative single-head attention (ISHA) mechanism for multi-UAV path pla…
Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learning
Shraman Pal, Mansi Uniyal, Jihong Park +5
In recent years, there have been great advances in the field of decentralized learning with private data. Federated learning (FL) and split learning (SL) are two spearheads possess…
Communication and Energy Efficient Slimmable Federated Learning via Superposition Coding and Successive Decoding
Hankyul Baek, Won Joon Yun, Soyi Jung +4
Mobile devices are indispensable sources of big data. Federated learning (FL) has a great potential in exploiting these private data by exchanging locally trained models instead of…
Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks
Hankyul Baek, Won Joon Yun, Yunseok Kwak +5
This paper aims to integrate two synergetic technologies, federated learning (FL) and width-adjustable slimmable neural network (SNN) architectures. FL preserves data privacy by ex…
Learning Emergent Random Access Protocol for LEO Satellite Networks
Ju-Hyung Lee, Hyowoon Seo, Jihong Park +2
A mega-constellation of low-altitude earth orbit (LEO) satellites (SATs) are envisaged to provide a global coverage SAT network in beyond fifth-generation (5G) cellular systems. LE…
Semantics-Native Communication with Contextual Reasoning
Hyowoon Seo, Jihong Park, Mehdi Bennis +1
Spurred by a huge interest in the post-Shannon communication, it has recently been shown that leveraging semantics can significantly improve the communication effectiveness across…
Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication
Won Joon Yun, Byungju Lim, Soyi Jung +4
In this article, we study the problem of air-to-ground ultra-reliable and low-latency communication (URLLC) for a moving ground user. This is done by controlling multiple unmanned…
Robust Reconfigurable Intelligent Surfaces via Invariant Risk and Causal Representations
Sumudu Samarakoon, Jihong Park, Mehdi Bennis
In this paper, the problem of robust reconfigurable intelligent surface (RIS) system design under changes in data distributions is investigated. Using the notion of invariant risk…