Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Beomyeol Jeon"'
Publikováno v:
INFOCOM Workshops
Federated learning is a promising framework for learning over decentralized data spanning multiple regions. This approach avoids expensive central training data aggregation cost and can improve privacy because distributed sites do not have to reveal
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::08cc0f29a6d4e3ceb9a6457dac0c05a2
http://arxiv.org/abs/2012.07183
http://arxiv.org/abs/2012.07183
Autor:
Xiaolan Ke, Jintao Jiang, Yitao Meng, Linda Cai, Indranil Gupta, Pallavi Srivastava, Beomyeol Jeon, Cong Xie
Publikováno v:
SoCC
Machine Learning graphs (or models) can be challenging or impossible to train when either devices have limited memory, or the models are large. Splitting the model graph across multiple devices, today, largely relies on learning-based approaches to g
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a1f16c25fd60909d7e4eacacaf8d5305
https://doi.org/10.1145/3419111.3421302
https://doi.org/10.1145/3419111.3421302
Autor:
Dahlia Malkhi, Youngseok Yang, Yingda Chen, Julia Wang, Raghu Ramakrishnan, Beysim Sezgin, Matteo Interlandi, Brian Cho, Taegeon Um, Mariia Mykhailova, Joseph Noor, Sergiy Matusevych, Brandon Myers, Joo Jeong, Gyewon Lee, Beomyeol Jeon, Sriram Rao, Chris Douglas, Carlo Curino, Tyson Condie, Russell Sears, Yunseong Lee, Tony Majestro, Byung-Gon Chun, Shravan Narayanamurthy, Andrew Chung, Markus Weimer
Publikováno v:
ACM Transactions on Computer Systems. 35:1-31
Resource Managers like YARN and Mesos have emerged as a critical layer in the cloud computing system stack, but the developer abstractions for leasing cluster resources and instantiating application logic are very low level. This flexibility comes at
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::496374bcfa7fc413ab6687fa9530a81c
https://doi.org/10.1145/3132037
https://doi.org/10.1145/3132037
Autor:
Brian Cho, Joo Yeon Kim, Gyeong-In Yu, Byung-Gon Chun, Hojin Park, Woo-Yeon Lee, Yunseong Lee, Markus Weimer, Beomyeol Jeon, Joo Jeong, Won Wook Song, Gunhee Kim
Publikováno v:
ICDCS
The performance of distributed machine learning systems is dependent on their system configuration. However, configuring the system for optimal performance is challenging and time consuming even for experts due to the diverse runtime factors such as
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::110542761247076215cea8eb1738524b
https://doi.org/10.1109/icdcs.2019.00203
https://doi.org/10.1109/icdcs.2019.00203