434 citations · 1k across the 53 of their papers we have counts for
55 papers
Over-the-Air Consensus for Distributed Vehicle Platooning Control (Extended version)
Jihoon Lee, Yonghoon Jang, Hansol Kim +2
A distributed control of vehicle platooning is referred to as distributed consensus (DC) since many autonomous vehicles (AVs) reach a consensus to move as one body with the same ve…
Communication-Efficient and Personalized Federated Lottery Ticket Learning
Sejin Seo, Seung-Woo Ko, Jihong Park +2
The lottery ticket hypothesis (LTH) claims that a deep neural network (i.e., ground network) contains a number of subnetworks (i.e., winning tickets), each of which exhibiting iden…
Mean-Field Game-Theoretic Edge Caching
Hyesung Kim, Jihong Park, Mehdi Bennis +2
In this book chapter, we study a problem of distributed content caching in an ultra-dense edge caching network (UDCN), in which a large number of small base stations (SBSs) prefetc…
Federated Knowledge Distillation
Hyowoon Seo, Jihong Park, Seungeun Oh +2
Distributed learning frameworks often rely on exchanging model parameters across workers, instead of revealing their raw data. A prime example is federated learning that exchanges…
Exploiting User Mobility for WiFi RTT Positioning: A Geometric Approach
Kyuwon Han, Seung Min Yu, Seong-Lyun Kim +1
Recently, round-trip time (RTT) measured by a fine-timing measurement protocol has received great attention in the area of WiFi positioning. It provides an acceptable ranging accur…
Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications
Jihong Park, Sumudu Samarakoon, Anis Elgabli +4
Machine learning (ML) is a promising enabler for the fifth generation (5G) communication systems and beyond. By imbuing intelligence into the network edge, edge nodes can proactive…
Mix2FLD: Downlink Federated Learning After Uplink Federated Distillation With Two-Way Mixup
Seungeun Oh, Jihong Park, Eunjeong Jeong +3
This letter proposes a novel communication-efficient and privacy-preserving distributed machine learning framework, coined Mix2FLD. To address uplink-downlink capacity asymmetry, l…
XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning
MyungJae Shin, Chihoon Hwang, Joongheon Kim +3
User-generated data distributions are often imbalanced across devices and labels, hampering the performance of federated learning (FL). To remedy to this non-independent and identi…
Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Learning
Han Cha, Jihong Park, Hyesung Kim +2
Traditional distributed deep reinforcement learning (RL) commonly relies on exchanging the experience replay memory (RM) of each agent. Since the RM contains all state observations…
Random Access with Opportunity Detection in Wireless Networks
Jinho Choi, Seung-Woo Ko, Koji Yamamoto +1
This letter proposes a novel random medium access control (MAC) based on a transmission opportunity prediction, which can be measured in a form of a conditional success probability…
Distilling On-Device Intelligence at the Network Edge
Jihong Park, Shiqiang Wang, Anis Elgabli +6
Devices at the edge of wireless networks are the last mile data sources for machine learning (ML). As opposed to traditional ready-made public datasets, these user-generated privat…
Multi-hop Federated Private Data Augmentation with Sample Compression
Eunjeong Jeong, Seungeun Oh, Jihong Park +3
On-device machine learning (ML) has brought about the accessibility to a tremendous amount of data from the users while keeping their local data private instead of storing it in a…
Federated Reinforcement Distillation with Proxy Experience Memory
Han Cha, Jihong Park, Hyesung Kim +2
In distributed reinforcement learning, it is common to exchange the experience memory of each agent and thereby collectively train their local models. The experience memory, howeve…
Communication-Efficient On-Device Machine Learning: Federated Distillation and Augmentation under Non-IID Private Data
Eunjeong Jeong, Seungeun Oh, Hyesung Kim +3
On-device machine learning (ML) enables the training process to exploit a massive amount of user-generated private data samples. To enjoy this benefit, inter-device communication o…
Privacy Preserving Multi-Server k-means Computation over Horizontally Partitioned Data
Riddhi Ghosal, Sanjit Chatterjee
The k-means clustering is one of the most popular clustering algorithms in data mining. Recently a lot of research has been concentrated on the algorithm when the dataset is divide…
Massive UAV-to-Ground Communication and its Stable Movement Control: A Mean-Field Approach
Hyesung Kim, Jihong Park, Mehdi Bennis +1
This paper proposes a real-time movement control algorithm for massive unmanned aerial vehicles (UAVs) that provide emergency cellular connections in an urban disaster site. While…
Asymptotic Analysis of Normalized SNR-Based Scheduling in Uplink Cellular Networks with Truncated Channel Inversion Power Control
Shotaro Kamiya, Koji Yamamoto, Seong-Lyun Kim +2
This paper provides the signal-to-interference-plus-noise ratio (SINR) complimentary cumulative distribution function (CCDF) and average data rate of the normalized SNR-based sched…
Opportunism in Dynamic Spectrum Access for 5G: A Concept and Its Application to Duplexing
Jeemin Kim, Soo-Min Kim, Han Cha +4
With the envisioned massive Internet-of-Things (IoT) era, one of the challenges for 5G wireless systems will be handling the unprecedented spectrum crunch. A potential solution has…
Mean-Field Game Theoretic Edge Caching in Ultra-Dense Networks
Hyesung Kim, Jihong Park, Mehdi Bennis +2
This paper investigates a cellular edge caching problem under a very large number of small base stations (SBSs) and users. In this ultra-dense edge caching network (UDCN), conventi…
Enhancing TCP End-to-End Performance in Millimeter-Wave Communications
Minho Kim, Seung-Woo Ko, Seong-Lyun Kim
Recently, millimeter-wave (mmWave) communications have received great attention due to the availability of large spectrum resources. Nevertheless, their impact on TCP performance h…
Revisiting Frequency Reuse towards Supporting Ultra-Reliable Ubiquitous-Rate Communication
Jihong Park, Dong Min Kim, Petar Popovski +1
One of the goals of 5G wireless systems stated by the NGMN alliance is to provide moderate rates (50+ Mbps) everywhere and with very high reliability. We term this service Ultra-Re…
Ultra-Dense Edge Caching under Spatio-Temporal Demand and Network Dynamics
Hyesung Kim, Jihong Park, Mehdi Bennis +2
This paper investigates a cellular edge caching design under an extremely large number of small base stations (SBSs) and users. In this ultra-dense edge caching network (UDCN), SBS…
Sense-and-Predict: Opportunistic MAC Based on Spatial Interference Correlation for Cognitive Radio Networks
Jeemin Kim, Seung-Woo Ko, Han Cha +1
Opportunity detection at secondary transmitters (TXs) is a key technique enabling cognitive radio (CR) networks. Such detection however cannot guarantee reliable communication at s…
Cooperative Transmissions in Ultra-Dense Networks under a Bounded Dual-Slope Path Loss Model
Yanpeng Yang, Ki Won Sung, Jihong Park +2
In an Ultra-dense network (UDN) where there are more base stations (BSs) than active users, it is possible that many BSs are instantaneously left idle. Thus, how to utilize these d…