104 citations · 414 across the 51 of their papers we have counts for
19 papers · 1 filter
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…
Robustness and Diversity Seeking Data-Free Knowledge Distillation
Pengchao Han, Jihong Park, Shiqiang Wang +1
Knowledge distillation (KD) has enabled remarkable progress in model compression and knowledge transfer. However, KD requires a large volume of original data or their representatio…
Explore-Before-Talk: Multichannel Selection Diversity for Uplink Transmissions in Machine-Type Communication
Jinho Choi, Jihong Park, Shiva Pokhrel
Improving the data rate of machine-type communication (MTC) is essential in supporting emerging Internet of things (IoT) applications ranging from real-time surveillance to edge ma…
Integrating LEO Satellites and Multi-UAV Reinforcement Learning for Hybrid FSO/RF Non-Terrestrial Networks
Ju-Hyung Lee, Jihong Park, Mehdi Bennis +1
A mega-constellation of low-altitude earth orbit (LEO) satellites (SATs) and burgeoning unmanned aerial vehicles (UAVs) are promising enablers for high-speed and long-distance comm…
When Wireless Communications Meet Computer Vision in Beyond 5G
Takayuki Nishio, Yusuke Koda, Jihong Park +2
This article articulates the emerging paradigm, sitting at the confluence of computer vision and wireless communication, to enable beyond-5G/6G mission-critical applications (auton…
Communication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMM
Chaouki Ben Issaid, Anis Elgabli, Jihong Park +2
In this paper, we propose a communication-efficiently decentralized machine learning framework that solves a consensus optimization problem defined over a network of inter-connecte…