Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Hankyul Baek"'
Publikováno v:
IEEE Access, Vol 12, Pp 108504-108514 (2024)
Multi-agent reinforcement learning (MARL) algorithms have been widely used for many applications requiring sequential decision-making to maximize the expected rewards through multi-agent cooperation. However, MARL faces significant challenges, partic
Externí odkaz:
https://doaj.org/article/1e7f711f153b4cf19071dc315e376ed9
Publikováno v:
IEEE Access, Vol 12, Pp 54732-54744 (2024)
A metaverse is composed of a physical-space and virtual-space, with the aim of having users in both the virtual reality and the real world experience. Prioritization is essential, but it is not straight-forwarded due to the limitation of computing re
Externí odkaz:
https://doaj.org/article/a83e6ccb25e1464695033411325f25ce
Publikováno v:
IEEE Open Journal of the Computer Society, Vol 4, Pp 243-252 (2023)
Real-time volumetric contents streaming on augmented reality (AR) devices should necessitate a balance between end-users' quality of experience (QoE) and the latency requirements. Lowering the quality of the volumetric contents to diminish the latenc
Externí odkaz:
https://doaj.org/article/b455bacc48c648ca9a29c247cb77d604
Publikováno v:
2023 International Conference on Information Networking (ICOIN).
Publikováno v:
2023 International Conference on Information Networking (ICOIN).
Publikováno v:
2022 IEEE VTS Asia Pacific Wireless Communications Symposium (APWCS).
Autor:
Won Joon Yun, Yunseok Kwak, Hankyul Baek, Soyi Jung, Mingyue Ji, Mehdi Bennis, Jihong Park, Joongheon Kim
Federated learning (FL) is a key enabler for efficient communication and computing, leveraging devices' distributed computing capabilities. However, applying FL in practice is challenging due to the local devices' heterogeneous energy, wireless chann
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::37b5b36284fc5963e7cdce2a03d22127
http://arxiv.org/abs/2203.14094
http://arxiv.org/abs/2203.14094
Autor:
Hankyul Baek, Won Joon Yun, Yunseok Kwak, Soyi Jung, Mingyue Ji, Mehdi Bennis, Jihong Park, Joongheon Kim
This paper aims to integrate two synergetic technologies, federated learning (FL) and width-adjustable slimmable neural network (SNN) architectures. FL preserves data privacy by exchanging the locally trained models of mobile devices. By adopting SNN
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a338bcf6a8ede1943ed99088ce72248e
http://urn.fi/urn:nbn:fi-fe2023021026716
http://urn.fi/urn:nbn:fi-fe2023021026716