Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Park, J. (Jihong)"'
Autor:
Lee, J.-H. (Ju-Hyung), Seo, H. (Hyowoon), Park, J. (Jihong), Bennis, M. (Mehdi), Ko, Y.-C. (Young-Chai), Kim, J. (Joongheon)
Publikováno v:
2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring).
A mega-constellation of low-altitude earth orbit (LEO) satellites (SATs) are envisaged to provide a global coverage SAT network in beyond fifth-generation (5G) cellular systems. However, such wide coverage rather makes it difficult to apply existing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9832142f5da83d7883c62e6c0423fa72
https://doi.org/10.1109/vtc2022-spring54318.2022.9860594
https://doi.org/10.1109/vtc2022-spring54318.2022.9860594
Autor:
Oh, S. (Seungeun), Park, J. (Jihong), Vepakomma, P. (Praneeth), Baek, S. (Sihun), Raskar, R. (Ramesh), Bennis, M. (Mehdi), Kim, S.-L. (Seong-Lyun)
Split learning (SL) is a promising distributed learning framework that enables to utilize the huge data and parallel computing resources of mobile devices. SL is built upon a model-split architecture, wherein a server stores an upper model segment th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2423::a6b2103c8f5d78c0182b5ce9776ef9dd
http://urn.fi/urn:nbn:fi-fe2023040334843
http://urn.fi/urn:nbn:fi-fe2023040334843
This study demonstrates the feasibility of image inpainting using both visual information and radio frequency (RF) signals. Recent developments in imaging and vision-based technologies using RF signals have revealed the potential of leveraging multim
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2423::f7412d1808afd5d1dc15e8062b82ac64
http://urn.fi/urn:nbn:fi-fe202301173368
http://urn.fi/urn:nbn:fi-fe202301173368
Autor:
Jeong, E. (Eunjeong), Oh, S. (Seungeun), Park, J. (Jihong), Kim, H. (Hyesung), Bennis, M. (Mehdi), Kim, S.-L. (Seong-Lyun)
To cope with the lack of on-device machine learning samples, this article presents a distributed data augmentation algorithm, coined federated data augmentation (FAug). In FAug, devices share a tiny fraction of their local data, i.e., seed samples, a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2423::dbc152b70984d491803356fde02022fb
http://urn.fi/urn:nbn:fi-fe2021122162702
http://urn.fi/urn:nbn:fi-fe2021122162702
Autor:
Park, J. (Jihong), Samarakoon, S. (Sumudu), Elgabli, A. (Anis), Kim, J. (Joongheon), Bennis, M. (Mehdi), Kim, S.-L. (Seong-Lyun), Debbah, M. (Mérouane)
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 proactively carry out decision-making and, thereby, react to local environmenta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2423::0e72fde1464ae5ee004ce136c986298c
http://urn.fi/urn:nbn:fi-fe2021082343805
http://urn.fi/urn:nbn:fi-fe2021082343805
Autor:
Elgabli, A. (Anis), Park, J. (Jihong), Bedi, A. S. (Amrit S.), Issaid, C. B. (Chaouki Ben), Bennis, M. (Mehdi), Aggarwal, V. (Vaneet)
In this article, we propose a communication-efficient decentralized machine learning (ML) algorithm, coined quantized group ADMM (Q-GADMM). To reduce the number of communication links, every worker in Q-GADMM communicates only with two neighbors, whi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2423::2a06c9a783f6dee8622815d08a26c5bc
http://urn.fi/urn:nbn:fi-fe202103096830
http://urn.fi/urn:nbn:fi-fe202103096830
While remote control over wireless connections is a key enabler for scalable control systems consisting of multiple actuator-sensor pairs, i.e., control systems, it entails two technical challenges. Due to the lack of wireless resources, only a limit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2423::fb987c78ea23fbe59b56b1330852afaa
http://urn.fi/urn:nbn:fi-fe202102195360
http://urn.fi/urn:nbn:fi-fe202102195360
Autor:
Cha, H. (Han), Park, J. (Jihong), Kim, H. (Hyesung), Bennis, M. (Mehdi), Kim, S.-L. (Seong-Lyun)
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 and action policy history, it may incur huge communication overhead w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2423::853bd2b5ec9d2b98518576f4a411c3f1
http://urn.fi/urn:nbn:fi-fe2020101684236
http://urn.fi/urn:nbn:fi-fe2020101684236
A mega-constellation of low-earth orbit (LEO) satellites has the potential to enable long-range communication with low latency. Integrating this with burgeoning unmanned aerial vehicle (UAV) assisted non-terrestrial networks will be a disruptive solu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2423::7f1d1cadfbfe6939af33bc8d22f20a80
http://urn.fi/urn:nbn:fi-fe202102255938
http://urn.fi/urn:nbn:fi-fe202102255938
This article proposes a communication-efficient decentralized deep learning algorithm, coined layer-wise federated group ADMM (L-FGADMM). To minimize an empirical risk, every worker in L-FGADMM periodically communicates with two neighbors, in which t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2423::695d91b0acb1048d619a235f1cbad463
http://urn.fi/urn:nbn:fi-fe2020102787858
http://urn.fi/urn:nbn:fi-fe2020102787858