
Choong Seon HongKyung Hee University · Department of Computer Science and Engineering
Choong Seon Hong
Keio University
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1,184
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Publications (1,184)
This paper aims to improve the robustness of a small global model while maintaining clean accuracy under adversarial attacks and non-IID challenges in federated learning. By leveraging the concise knowledge embedded in the class probabilities from a pre-trained model for both clean and adversarial image classification, we propose a Pre-trained Mode...
Chao Wang Yu Han Long Zhang- [...]
Zhu Han
The collaboration of computing powers (CPs) among unmanned aerial vehicles (UAVs)-mounted edge servers is essential to handle data-intensive tasks of user equipments (UEs). This paper presents a multi-UAV computing power network (CPN) that orchestrates the CP resources of edge servers to cooperatively process tasks through collaborative computing....
Semantic Communication (SemCom), notable for ensuring quality of service by jointly optimizing source and channel coding, effectively extracts data semantics, eliminates redundant information, and mitigates noise effects from wireless channel. However, most studies overlook multiple user scenarios and resource availability, limiting real-world appl...
Federated Learning (FL) has emerged as a decentralized machine learning technique, allowing clients to train a global model collaboratively without sharing private data. However, most FL studies ignore the crucial challenge of heterogeneous domains where each client has a distinct feature distribution, which is common in real-world scenarios. Proto...
6G NTNs significantly enhance Earth observation capabilities, despite challenges such as latency and bandwidth limitations. This research introduces a robust integrated framework that harnesses multi-source semantic communication and intelligent denoising, effectively boosting efficiency, resilience, and data quality.
Data-intensive smart applications are currently driving the emergence of edge-enabled computing power network (Edge-CPN) by orchestrating the computing powers (CPs) of edge servers, enabling the converged computing and networking at the edge. Besides, the proliferation of these applications and various smart devices (SDs) is arousing great interest...
Federated learning (FL) is a distributed training technology that enhances data privacy in mobile edge networks by allowing data owners to collaborate without transmitting raw data to the edge server. However, data heterogeneity and adversarial attacks pose challenges to develop an unbiased and robust global model for edge deployment. To address th...
Medical image segmentation is crucial in assisting medical doctors in making diagnoses and enabling accurate automatic diagnosis. While advanced convolutional neural networks (CNNs) excel in segmenting regions of interest with pixel-level precision, they often struggle with long-range dependencies, which is crucial for enhancing model performance....
Beyond the success of Contrastive Language-Image Pre-training (CLIP), recent trends mark a shift toward exploring the applicability of lightweight vision-language models for resource-constrained scenarios. These models often deliver suboptimal performance when relying solely on a single image-text contrastive learning objective, spotlighting the ne...
This article introduces a novel
attentive driving
framework in intelligent transportation systems (ITS) to investigate the influence of cognitive behavior on distracting driving activities that lead to inattention while driving. Therefore, this work proposes a holistic computational and communication framework that can monitor on-compartment real...
Integrating terrestrial and non-terrestrial networks has emerged as a promising paradigm to fulfill the constantly growing demand for connectivity, low transmission delay, and quality of services (QoS). This integration brings together the strengths of the reliability of terrestrial networks, broad coverage and service continuity of non-terrestrial...
The Segment Anything Model (SAM), developed by Meta AI Research, represents a significant breakthrough in computer vision, offering a robust framework for image and video segmentation. This survey provides a comprehensive exploration of the SAM family, including SAM and SAM 2, highlighting their advancements in granularity and contextual understand...
Low Earth orbit (LEO) satellites are capable of gathering abundant Earth observation data (EOD) to enable different Internet of Things (IoT) applications. However, to accomplish an effective EOD processing mechanism, it is imperative to investigate: 1) the challenge of processing the observed data without transmitting those large-size data to the g...
In this paper, cyber-attack prevention for the prosumer-based electric vehicle (EV) charging stations (EVCSs) is investigated, which covers two aspects: 1) cyber-attack detection on prosumers' network traffic (NT) data, and 2) cyber-attack intervention. To establish an effective prevention mechanism, several challenges need to be tackled, for insta...
With the proliferation of the Internet of Things (IoT) and the rising interconnectedness of devices, network security faces significant challenges, especially from anomalous activities. While traditional machine learning-based intrusion detection systems (ML-IDS) effectively employ supervised learning methods, they possess limitations such as the r...
In this paper, a novel generative adversarial imitation learning (GAIL)-powered policy learning approach is proposed for optimizing beamforming, spectrum allocation, and remote user equipment (RUE) association in NTNs. Traditional reinforcement learning (RL) methods for wireless network optimization often rely on manually designed reward functions,...
The sixth-generation (6G) non-terrestrial networks (NTNs) are crucial for real-time monitoring in critical applications like disaster relief. However, limited bandwidth, latency, rain attenuation, long propagation delays, and co-channel interference pose challenges to efficient satellite communication. Therefore, semantic communication (SC) has eme...
In this study, we explore the efficacy of advanced pre-trained architectures, such as Vision Transformers (ViT), ConvNeXt, and Swin Transformers in enhancing Federated Domain Generalization. These architectures capture global contextual features and model long-range dependencies, making them promising candidates for improving cross-domain generaliz...
The proliferation of low-Earth orbit (LEO) satellites in 6G non-terrestrial networks (6G-NTNs) promises ubiquitous network connectivity for real-time communication, monitoring, and time-critical applications. However, achieving low downlink latency (the time it takes for data to travel from the satellite to the gateway) is crucial for these applica...
The proliferation of data-intensive and low-latency applications has driven the development of multi-access edge computing (MEC) as a viable solution to meet the increasing demands for high-performance computing and storage capabilities at the network edge. Despite the benefits of MEC, challenges such as obstructions cause non-line-of-sight (NLoS)...
Connected and autonomous vehicles (CAVs) can reduce human errors in traffic accidents, increase road efficiency, and execute various tasks ranging from delivery to smart city surveillance. Reaping these benefits requires CAVs to autonomously navigate to target destinations. To this end, each CAV's navigation controller must leverage the information...
Recently, significant research efforts have been initiated to enable the next-generation -- the sixth-generation (6G) -- wireless systems. In this article, we present a vision of the metaverse toward effectively enabling the development of 6G wireless systems. A metaverse uses virtual representation (e.g., digital twin), digital avatars, and intera...
Earth observation satellites generate large amounts of real-time data for monitoring and managing time-critical events such as disaster relief missions. This presents a major challenge for satellite-to-ground communications operating under limited bandwidth capacities. This paper explores semantic communication (SC) as a potential alternative to tr...
Earth observation satellites generate large amounts of real-time data for monitoring and managing time-critical events such as disaster relief missions. This presents a major challenge for satellite-to-ground communications operating under limited bandwidth capacities. This paper explores semantic communication (SC) as a potential alternative to tr...
With the proliferation of the Internet of Things (IoT) and the rising interconnectedness of devices, network security faces significant challenges, especially from anomalous activities. While traditional machine learning-based intrusion detection systems (ML-IDS) effectively employ supervised learning methods, they possess limitations such as the r...
Sixth-generation (6G) networks leverage simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) to overcome the limitations of traditional RISs. STAR-RISs offer 360-degree full-space coverage and optimized transmission and reflection for enhanced network performance and dynamic control of the indoor propagation en...
The past few decades have witnessed an upsurge in data, forming the foundation for data-hungry, learning-based AI technology. Conversational agents, often referred to as AI chatbots, rely heavily on such data to train large language models (LLMs) and generate new content (knowledge) in response to user prompts. With the advent of OpenAI's ChatGPT,...
Sixth-generation wireless networks are required to satisfy the ever-increasing demands of diverse applications to guarantee power savings, energy efficiency, and mass connectivity. To accomplish these goals, in this paper, an artificial intelligence (AI)-based holographic MIMO (HMIMO)-empowered cell-free (CF) network is proposed while leveraging in...
The 6G wireless communication networks need an
intelligent networking system to meet the ever-increasing demands of various applications and mobile devices to ensure power savings, energy efficiency (EE), high integration of devices, and mass connection. To achieve these aims, an artificial intelligence (AI)-based holographic MIMO (HMIMO)-aided cel...
The 6G wireless communication networks need an intelligent networking system to meet the ever-increasing demands of various applications and mobile devices to ensure power savings, energy efficiency (EE), high integration of devices, and mass connection. To achieve these aims, an artificial intelligence (AI)-based holographic MIMO (HMIMO)-aided cel...
The deployment of deep learning architectures on low-computational resource devices is challenging due to their high number of parameters and computational complexity. These heavy and complex architectures result in increased latency in real-time applications. However, splitting the deep architecture in a way that parallelizes the forward propagati...
The evolution of video generation from text, starting with animating MNIST numbers to simulating the physical world with Sora, has progressed at a breakneck speed over the past seven years. While often seen as a superficial expansion of the predecessor text-to-image generation model, text-to-video generation models are developed upon carefully engi...
Semantic communication, notable for ensuring quality of service by jointly optimizing source and channel coding, effectively extracts data semantics, reduces transmission length, and mitigates channel noise. However, most studies overlook multi-user scenarios and resource availability, limiting real-world application. This paper addresses this gap...
Advancements in 6G wireless technology have elevated the importance of beamforming, especially for attaining ultra-high data rates via millimeter-wave (mmWave) frequency deployment. Although promising, mmWave bands require substantial beam training to achieve precise beamforming. While initial deep learning models that use RGB camera images demonst...
Semantic communication has gained significant attention from researchers as a promising technique to replace conventional communication in the next generation of communication systems, primarily due to its ability to reduce communication costs. However, little literature has studied its effectiveness in multi-user scenarios, particularly when there...
Integrating terrestrial and non-terrestrial networks has emerged as a promising paradigm to fulfill the constantly growing demand for connectivity, low transmission delay, and quality of services (QoS). This integration brings together the strengths of terrestrial and non-terrestrial networks, such as the reliability of terrestrial networks, broad...
In the rapidly evolving landscape of Web3 and blockchain technologies, decentralized autonomous organizations (DAOs) have emerged as innovative structures that operate autonomously through blockchain and smart contracts, eliminating the need for centralized control. The federated learning (FL) process, akin to an information flow under structured t...
While astonishingly capable, large Language Models (LLM) can sometimes produce outputs that deviate from human expectations. Such deviations necessitate an alignment phase to prevent disseminating untruthful, toxic, or biased information. Traditional alignment methods based on reinforcement learning often struggle with the identified instability, w...
Semantic communication has emerged as a pillar for the next generation of communication systems due to its capabilities in alleviating data redundancy. Most semantic communication systems are built upon advanced deep learning models whose training performance heavily relies on data availability. Existing studies often make unrealistic assumptions o...
The upcoming 6G wireless communication networks
are anticipated to offer extensive mobile connectivity, faster
data services with reduced power consumption, and seamless
integration among different technologies for providing effective
beamforming. To accomplish these aims, a transfer learning
empowered AI framework is proposed to allocate the power...
Satellite systems face a significant challenge in effectively utilizing limited communication resources to meet the demands of ground network traffic, characterized by asymmetrical spatial distribution and time-varying characteristics. Moreover, the coverage range and signal transmission distance of low Earth orbit (LEO) satellites are restricted b...
Multi-access Edge Computing (MEC) addresses computational and battery limitations in devices by allowing them to offload computation tasks. To overcome the difficulties in establishing line-of-sight connections, integrating unmanned aerial vehicles (UAVs) has proven beneficial, offering enhanced data exchange, rapid deployment, and mobility. The ut...
A digital twin uses a virtual model of the physical system to fulfill the diverse requirements (e.g., latency, reliability, quality of physical experience) for emerging vehicular network applications. Although a twin-based implementation of vehicular networks can offer performance optimization, modeling a digital twin is a significantly challenging...
In the swiftly evolving landscape of 6G wireless communication, beamforming plays a pivotal role, particularly when exploiting millimeter-wave and terahertz frequency bands for achieving ultra-high data rates. However, these bands bring about challenges in beam training overheads, which can hinder Ultra-Reliable Low-Latency Communication (URLLC), e...
This letter considers a reconfigurable intelligent surface (RIS)-aided indoor visible light communication system, where a mirror array-based RIS is deployed to assist the communication from a light-emitting diode (LED) to multiple user terminals (UTs). We aim to maximize the sum-rate in an entire serving period by jointly optimizing the orientation...
The future 6G wireless communication system demands an intelligent network with enhanced performance and higher network capacity. In this context, an intelligent omni surface (IOS)-aided Cell-Free (ICF) network is introduced that expands network coverage by delivering services on both sides of the IOS while minimizing power consumption. Utilizing t...
This paper proposes an artificial intelligence (AI) framework that leverages integrated sensing and communication (ISAC), aided by the age of sensing (AoS) to ensure the timely location updates of the users for a holographic MIMO (HMIMO)-enabled wireless network. The AI-driven framework guarantees optimal power allocation for efficient beamforming...
Despite the basic premise that next-generation wireless networks (e.g., 6G) will be artificial intelligence (AI)-native, to date, most existing efforts remain either qualitative or incremental extensions to existing “AI for wireless” paradigms. Indeed, creating AI-native wireless networks faces significant technical challenges due to the limitation...
Future communication networks are expected to provide improved throughput data services with minimal power for beamforming. The location-dependent and task-oriented resource allocation approach for holographic beamforming ensures the improvement of the channel capacity for the users by activating the required number of grids from the holographic gr...
The sixth-generation wireless networks are required to satisfy the ever-increasing demands of diverse applications to guarantee power savings, energy efficiency, mass connectivity, and higher integration of devices. To accomplish these goals, in this paper, an artificial intelligence (AI)-based holographic MIMO (HMIMO)-empowered cell-free (CF) netw...
Federated learning (FL) is a privacy-preserving distributed framework for collaborative model training in edge networks. However, challenges such as vulnerability to adversarial examples and non-independent and identically distributed (non-IID) data across devices hinder the deployment of adversarially robust and accurate models at the edge. While...
Multi-access edge computing (MEC)-enabled integrated space-air-ground (SAG) networks have drawn much attention recently, as they can provide communication and computing services to wireless devices in areas that lack terrestrial base stations (TBSs). Leveraging the ample bandwidth in the terahertz (THz) spectrum, in this paper, we propose MEC-enabl...
In this paper, cyber-attack prevention for the prosumer-based electric vehicle (EV) charging stations (EVCSs) is investigated, which covers two aspects: 1) cyber-attack detection on prosumers’ network traffic (NT) data, and 2) cyber-attack intervention. To establish an effective prevention mechanism, several challenges need to be tackled, for insta...
Integrated terrestrial-non-terrestrial networks have recently gained much attention because they can bridge the gap between the conventional terrestrial infrastructure and non-terrestrial networks. In addition to seamless connectivity, such networks can offer edge computing services to the users with real-time data processing demand. In this paper,...
Multi-access Edge Computing (MEC) addresses computational and battery limitations in devices by allowing them to offload computation tasks. To overcome the difficulties in establishing line-of-sight connections, integrating unmanned aerial vehicles (UAVs) has proven beneficial, offering enhanced data exchange, rapid deployment, and mobility. The ut...
Segment anything model (SAM) addresses two practical yet challenging segmentation tasks: segment anything (SegAny), which utilizes a certain point to predict the mask for a single object of interest, and segment everything (SegEvery), which predicts the masks for all objects on the image. What makes SegAny slow for SAM is its heavyweight image enco...
Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) for sixth-generation (6G) wireless networks. Thus, a reliable XAI twin system becomes essential to discretizing the physical behavior of the Internet of Everything (IoE) and identifying the reasons behind that beha...
p>Recent trends in emerging applications have motivated researchers to design advanced wireless systems to meet their evolving requirements. These emerging applications include digital healthcare, intelligent transportation systems, Industry 5.0, and more. To address the evolving requirements, leveraging a metaverse to empower $6$G wireless systems...