434 citations · 490 across the 12 of their papers we have counts for
12 papers
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…
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…
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…
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…
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…
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…
Cognitive Random Access for Internet-of-Things Networks
Hyesung Kim, Seung-Woo Ko, Seong-Lyun Kim
This paper focuses on cognitive radio (CR) internet- of-things (IoT) networks where spectrum sensors are deployed for IoT CR devices, which do not have enough hardware capability t…