Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
SeMI, RCS, RCC, NS, SR (Joint) |
2023-07-12 13:55 |
Osaka |
Osaka University Nakanoshima Center + Online (Primary: On-site, Secondary: Online) |
Radio Propagation Graph Representation Learning Katsuya Suto, Shinsuke Bannai, Koya Sato, Takeo Fujii (UEC) |
[more] |
|
SR |
2023-05-12 10:25 |
Hokkaido |
Center of lifelong learning Kiran (Higashi Muroran) (Primary: On-site, Secondary: Online) |
A Study on Adaptive Client/Miner Selection for Fast and Accurate Blockchain-Decentralized Federated Learning Yuta Tomimasu, Koya Sato (UEC) SR2023-17 |
Decentralized federative learning with blockchain is a learning method in which the model in federated learning is manag... [more] |
SR2023-17 pp.83-88 |
SR |
2023-05-12 13:55 |
Hokkaido |
Center of lifelong learning Kiran (Higashi Muroran) (Primary: On-site, Secondary: Online) |
[Invited Talk]
Federated Learning-Inspired Gaussian Process Regression: Low Latency Design and Its Application to Radio Map Construction Koya Sato (UEC) SR2023-20 |
Gaussian process regression (GPR) is a non-parametric method that optimizes regression analysis for Gaussian process dat... [more] |
SR2023-20 p.91 |
RCS, SR, SRW (Joint) |
2023-03-01 13:55 |
Tokyo |
Tokyo Institute of Technology, and Online (Primary: On-site, Secondary: Online) |
[Invited Lecture]
Modeling of Geospatial Data for Digital Twin Wireless Systems Koya Sato (UEC) SR2022-83 |
Over the last decade, there has been a wide range of discussions on digital-twin wireless systems. The digital-twin wire... [more] |
SR2022-83 p.3 |
SR, UWT (Joint) |
2023-01-27 10:55 |
Tokyo |
Takanawa Campus, Tokai Univ. (Primary: On-site, Secondary: Online) |
A Study on Reinforcement Learning-Based Adaptive Random Walk SGD for Attack-Resilient Decentralized Federated Learning Masakazu Okamoto (Tokyo Univ. of Science), Koya Sato (UEC), Keiichi Iwamura (Tokyo Univ. of Science) SR2022-76 |
[more] |
SR2022-76 pp.20-27 |
NS, ICM, CQ, NV (Joint) |
2022-11-24 09:55 |
Fukuoka |
Humanities and Social Sciences Center, Fukuoka Univ. + Online (Primary: On-site, Secondary: Online) |
Highly Accurate Privacy-Enhanced Federated Learning Using Data On The Server Yuta Kakizaki (TUS), Koya Sato (UEC), Keiichi Iwamura (TUS) NS2022-100 |
Federated learning is a cooperative machine learning approach that prohibits disclosing training data from distributed d... [more] |
NS2022-100 pp.1-6 |
IT, EMM |
2022-05-18 14:20 |
Gifu |
Gifu University (Primary: On-site, Secondary: Online) |
[Invited Talk]
Decentralized Federated Learning: Fundamentals, Research Trends and Open Issues in Wireless Channels Koya Sato (UEC) IT2022-14 EMM2022-14 |
The expansion of machine learning applications has raised novel concerns, such as data privacy and communication costs. ... [more] |
IT2022-14 EMM2022-14 p.73 |
SR |
2022-05-13 10:30 |
Tokyo |
NICT Koganei (Primary: On-site, Secondary: Online) |
[Short Paper]
On Automated Indoor Wireless Simulation with Image Sensor and 3D Reconstruction Koya Sato (UEC), Norisato Suga (SIT), Yoshihiro Maeda (Tokyo Univ. of Science) SR2022-12 |
[more] |
SR2022-12 pp.55-57 |
RCS, SR, SRW (Joint) |
2022-03-02 13:05 |
Online |
Online |
[Invited Lecture]
Radio Map Extrapolation Using Compensated Empirical CDF under Interference-Limited Observations Keita Katagiri, Koya Sato (UEC), Kei Inage (TMCIT), Takeo Fujii (UEC) SR2021-92 |
[more] |
SR2021-92 pp.28-35 |
RCS, SR, SRW (Joint) |
2022-03-04 09:45 |
Online |
Online |
[Short Paper]
On Implementation of Radio Propagation Simulator with Open 3D City Models Koya Sato (UEC) SR2021-113 |
In radio propagation simulations, the acquisition of precise information such as structures and topography is a signific... [more] |
SR2021-113 pp.121-123 |
SIP |
2021-08-23 14:50 |
Online |
Online |
[Invited Talk]
Visualizing Wireless Environments: An Introduction of Spatial Statistics and Its Extensions to Multi-Dimensional Interpolation Koya Sato (Tokyo Univ. of Science) SIP2021-30 |
The radio map construction has been attracting attention. This technology visualizes the state of the wireless environme... [more] |
SIP2021-30 pp.12-17 |
RCS, SR, NS, SeMI, RCC (Joint) |
2021-07-16 09:25 |
Online |
Online |
Improving the Runtime Performance of Decentralized Machine Learning on Wireless Channels via Rate Adaptation Koya Sato (Tokyo Univ. of Science), Daisuke Sugimura (Tsuda Univ.) RCS2021-94 |
This paper presents a communication strategy for improving the runtime of decentralized machine learning over wireless n... [more] |
RCS2021-94 pp.80-85 |
RCS, SR, NS, SeMI, RCC (Joint) |
2021-07-16 10:55 |
Online |
Online |
A Study on Decentralized Machine Learning with Differential Privacy based on Input Perturbation Masakazu Okamoto, Koya Sato, Keiichi Iwamura (Tokyo Univ. of Science) SR2021-34 |
Distributed machine learning eliminates the need for users to disclose their data to the out of the terminal since train... [more] |
SR2021-34 pp.67-72 |
RCS, SR, NS, SeMI, RCC (Joint) |
2021-07-16 13:25 |
Online |
Online |
An Evaluation of Learning Accuracy in Federated Learning with Local Differential Privacy Yuta Kakizaki, Koya Sato, Keiichi Iwamura (Tokyo Univ. of Science) SR2021-37 |
In federated learning, where each device learns cooperatively without disclosing the training data, the privacy level ca... [more] |
SR2021-37 pp.87-93 |
SR |
2020-11-20 10:00 |
Online |
Online |
Compensation of Clutter Loss Considering Antenna Height Difference and Dominant Path for Spectrum Sharing Sunao Miyamoto, Keita Katagiri (UEC), Koya Sato (TUS), Koichi Adachi, Takeo Fujii (UEC) SR2020-39 |
In recent years, spectrum sharing technology is attracting attention to solve the problem of spectrum shortage. In order... [more] |
SR2020-39 pp.108-113 |
SR |
2020-11-20 14:05 |
Online |
Online |
[Panel Discussion]
Data-Driven Radio Propagation Estimation for Spectrum Sharing: Trends and Challenges Koya Sato (TUS) SR2020-45 |
In the field of spectrum sharing, spectrum database has been recognized as a practical enabler for estimating and managi... [more] |
SR2020-45 pp.146-151 |
SR, NS, SeMI, RCC, RCS (Joint) |
2020-07-10 11:35 |
Online |
Online |
Experimental Verification of Shadowing Classification for Measurement-based Spectrum Database Keita Katagiri (UEC), Koya Sato (TUS), Kei Inage (TMCIT), Takeo Fujii (UEC) SR2020-20 |
As a hybrid estimation method of the radio environment, we have proposed a shadowing classifier. In the shadowing classi... [more] |
SR2020-20 pp.63-70 |
RISING (2nd) |
2019-11-27 13:55 |
Tokyo |
Fukutake Learning Theater, Hongo Campus, Univ. Tokyo |
[Poster Presentation]
Clustering of Signal Power Distribution Toward Low Storage Crowdsourced Spectrum Database Yoji Uesugi, Keita Katagiri (UEC), Koya Sato (TUS), Takeo Fujii (TMCIT), Takeo Fujii (UEC) |
[more] |
|
SeMI, RCS, NS, SR, RCC (Joint) |
2019-07-11 10:40 |
Osaka |
I-Site Nanba(Osaka) |
[Poster Presentation]
A Study on Efficient Secure Routing Protocol based on AODV for Large Scale Ad Hoc Networks Yuma Shibasaki, Koya Sato, Keiichi Iwamura (TUS) RCC2019-31 NS2019-67 RCS2019-124 SR2019-43 SeMI2019-40 |
This paper proposes a secure routing protocol based on Ad hoc On-demand Distance Vector (AODV) that achieves both securi... [more] |
RCC2019-31 NS2019-67 RCS2019-124 SR2019-43 SeMI2019-40 pp.101-106(RCC), pp.127-132(NS), pp.123-128(RCS), pp.133-138(SR), pp.115-120(SeMI) |
RCS, SR, SRW (Joint) |
2019-03-06 15:40 |
Kanagawa |
YRP |
[Invited Lecture]
Spectrum Sharing based on Radio Environment Map: Theory and Open Issues Koya Sato (Tokyo Univ. of Science) SR2018-127 |
This paper summarizes the radio environment map (REM) for spatial spectrum sharing. REM contains spatial distribution of... [more] |
SR2018-127 pp.43-49 |