Seong-Lyun Kim

research

∙ 09/20/2023

Language-Oriented Communication with Semantic Coding and Knowledge Distillation for Text-to-Image Generation

By integrating recent advances in large language models (LLMs) and gener...

0 Hyelin Nam, et al. ∙

research

∙ 09/08/2023

Sequential Semantic Generative Communication for Progressive Text-to-Image Generation

This paper proposes new framework of communication system leveraging pro...

0 Hyelin Nam, et al. ∙

research

∙ 04/17/2023

SplitAMC: Split Learning for Robust Automatic Modulation Classification

Automatic modulation classification (AMC) is a technology that identifie...

0 Jihoon Park, et al. ∙

research

∙ 12/13/2022

Enabling the Wireless Metaverse via Semantic Multiverse Communication

Metaverse over wireless networks is an emerging use case of the sixth ge...

0 Jihong Park, et al. ∙

research

∙ 11/15/2022

Enabling AI Quality Control via Feature Hierarchical Edge Inference

With the rise of edge computing, various AI services are expected to be ...

0 Jinhyuk Choi, et al. ∙

research

∙ 11/14/2022

Joint Data Deepening-and-Prefetching for Energy-Efficient Edge Learning

The vision of pervasive machine learning (ML) services can be realized b...

0 Sujin Kook, et al. ∙

research

∙ 11/11/2022

Over-the-Air Consensus for Distributed Vehicle Platooning Control (Extended version)

A distributed control of vehicle platooning is referred to as distribute...

0 Jihoon Lee, et al. ∙

research

∙ 10/28/2022

Differentially Private CutMix for Split Learning with Vision Transformer

Recently, vision transformer (ViT) has started to outpace the convention...

0 Seungeun Oh, et al. ∙

research

∙ 07/08/2022

Towards Semantic Communication Protocols: A Probabilistic Logic Perspective

Classical medium access control (MAC) protocols are interpretable, yet t...

7 Sejin Seo, et al. ∙

research

∙ 07/01/2022

Visual Transformer Meets CutMix for Improved Accuracy, Communication Efficiency, and Data Privacy in Split Learning

This article seeks for a distributed learning solution for the visual tr...

16 Sihun Baek, et al. ∙

research

∙ 02/10/2022

Two-Stage Deep Anomaly Detection with Heterogeneous Time Series Data

We introduce a data-driven anomaly detection framework using a manufactu...

0 Kyeong-Joong Jeong, et al. ∙

research

∙ 04/26/2021

Communication-Efficient and Personalized Federated Lottery Ticket Learning

The lottery ticket hypothesis (LTH) claims that a deep neural network (i...

0 Sejin Seo, et al. ∙

research

∙ 03/20/2021

Most Efficient Sensor Network Protocol for a Permanent Natural Disaster Monitoring System

To minimize enormous havoc from disasters, permanent environment monitor...

0 Changmin Lee, et al. ∙

research

∙ 01/01/2021

Mean-Field Game-Theoretic Edge Caching

In this book chapter, we study a problem of distributed content caching ...

0 Hyesung Kim, et al. ∙

research

∙ 11/07/2020

Exploiting User Mobility for WiFi RTT Positioning: A Geometric Approach

Due to the massive deployment of WiFi APs and its accessibility to vario...

0 Kyuwon Han, et al. ∙

research

∙ 11/04/2020

Federated Knowledge Distillation

Distributed learning frameworks often rely on exchanging model parameter...

0 Hyowoon Seo, et al. ∙

research

∙ 08/06/2020

Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications

Machine learning (ML) is a promising enabler for the fifth generation (5...

0 Jihong Park, et al. ∙

research

∙ 06/17/2020

Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup

This letter proposes a novel communication-efficient and privacy-preserv...

0 Seungeun Oh, et al. ∙

research

∙ 06/09/2020

XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning

User-generated data distributions are often imbalanced across devices an...

0 MyungJae Shin, et al. ∙

research

∙ 06/01/2020

Understanding Uncertainty of Edge Computing: New Principle and Design Approach

Due to the edge's position between the cloud and the users, and the rece...

0 Sejin Seo, et al. ∙

research

∙ 05/13/2020

Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning

Traditional distributed deep reinforcement learning (RL) commonly relies...

0 Han Cha, et al. ∙

research

∙ 05/13/2020

Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Leargning

Traditional distributed deep reinforcement learning (RL) commonly relies...

0 Han Cha, et al. ∙

research

∙ 04/27/2020

Demo: A Reinforcement Learning-based Flexible Duplex System for B5G with Sub-6 GHz

In this paper, we propose a reinforcement learning-based flexible duplex...

0 Soo-Min Kim, et al. ∙

research

∙ 04/08/2020

A Public Safety Framework for Immersive Aerial Monitoring through 5G Commercial Network

Are 5G connection and UAVs merely parts of an extravagant and luxurious ...

0 Sejin Seo, et al. ∙

research

∙ 12/13/2019

Random Access with Opportunity Detection in Wireless Networks

This letter proposes a novel random medium access control (MAC) based on...

0 Jinho Choi, et al. ∙

research

∙ 08/16/2019

Distilling On-Device Intelligence at the Network Edge

Devices at the edge of wireless networks are the last mile data sources ...

0 Jihong Park, et al. ∙

research

∙ 07/15/2019

Federated Reinforcement Distillation with Proxy Experience Memory

In distributed reinforcement learning, it is common to exchange the expe...

0 Han Cha, et al. ∙

research

∙ 07/15/2019

Multi-hop Federated Private Data Augmentation with Sample Compression

On-device machine learning (ML) has brought about the accessibility to a...

1 Eunjeong Jeong, et al. ∙

research

∙ 11/28/2018

Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data

On-device machine learning (ML) enables the training process to exploit ...

0 Eunjeong Jeong, et al. ∙

research

∙ 08/12/2018

On-Device Federated Learning via Blockchain and its Latency Analysis

In this letter, we propose a block-chained federated learning (BlockFL) ...

0 Hyesung Kim, et al. ∙

research

∙ 03/08/2018

Massive UAV-to-Ground Communication and its Stable Movement Control: A Mean-Field Approach

This paper proposes a real-time movement control algorithm for massive u...

0 Hyesung Kim, et al. ∙

research

∙ 02/06/2018

Asymptotic Analysis of Normalized SNR-Based Scheduling in Uplink Cellular Networks with Truncated Channel Inversion Power Control

This paper provides the signal-to-interference-plus-noise ratio (SINR) c...

0 Shotaro Kamiya, et al. ∙

research

∙ 02/04/2018

Sense-and-Predict: Harnessing Spatial Interference Correlation for Opportunistic Access in Cognitive Radio Networks

Cognitive radio (CR) is a key enabler realizing 5G networks to achieve h...

0 Jeemin Kim, et al. ∙

research

∙ 01/23/2018

Mean-Field Game Theoretic Edge Caching in Ultra-Dense Networks

This paper investigates a cellular edge caching problem under a very lar...

0 Hyesung Kim, et al. ∙

research

∙ 01/22/2018

Opportunism in Dynamic Spectrum Access for 5G: A Concept and Its Application to Duplexing

With the envisioned massive Internet-of-Things (IoT) era, one of the cha...

0 Jeemin Kim, et al. ∙

research

∙ 12/02/2017

Millimeter-Wave Interference Avoidance via Building-Aware Associations

Signal occlusion by building blockages is a double-edged sword for the p...

0 Jeemin Kim, et al. ∙

Seong-Lyun Kim

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