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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IN, RCS, NV (Joint) |
2024-05-31 09:00 |
Fukuoka | Fukuoka University | [Tutorial Lecture]
Decision making via End-to-End Lossy Distributed Wireless Cooperative Networks
-- A Distributed Hypothesis Testing based Formulation -- Tad Matsumoto (IMT-Atlantique, JAIST)  RCS2024-23 |
(To be available after the conference date) [more] | RCS2024-23 pp.34-35 |
| ICM, IPSJ-IOT, IPSJ-CSEC | 2024-05-31 09:00 |
Tottori | (Primary: On-site, Secondary: Online) |
Failure Point Localization Technique with Anomaly Detection Reiko Kondo, Takeshi Kodama, Takashi Shiraishi (FSAS TECHNOLOGIES) ICM2024-4 |
(To be available after the conference date) [more] | ICM2024-4 pp.15-20 |
| NS | 2023-10-06 15:20 |
Hokkaido | Hokkaidou University + Online (Primary: On-site, Secondary: Online) |
Incentive Mechanism Considering Heterogeneous Privacy Demand Level in Federated Learning with Differential Privacy Shota Miyagoshi, Takuji Tachibana (Univ. Fukui) NS2023-104 |
In federated learning, where multiple data owners participate as clients to perform machine learning, each client shares... [more] | NS2023-104 pp.162-167 |
| NS | 2023-10-06 15:45 |
Hokkaido | Hokkaidou University + Online (Primary: On-site, Secondary: Online) |
Experiment of Group Construction for Location-Based Distributed Machine Learning Ryota Hasegawa (SIT), Shota Ono (UTokyo), Taku Yamazaki, Takumi Miyoshi (SIT) NS2023-105 |
Distributed machine learning (DML), which executes learning process by cooperating with multiple computers via a network... [more] | NS2023-105 pp.168-171 |
| 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 |
| ICM | 2023-03-17 14:45 |
Okinawa | Okinawa Prefectural Museum and Art Museum (Primary: On-site, Secondary: Online) |
A Location-based Group Construction Method for Distributed Machine Learning Ryota Hasegawa (SIT), Shota Ono (Univ. of Tokyo), Taku Yamazaki, Takumi Miyoshi (SIT) ICM2022-60 |
Recently, machine learning (ML) has been utilized in various situations as a technology for learning from large amounts ... [more] | ICM2022-60 pp.101-104 |
| RCS, SR, SRW (Joint) |
2023-03-03 09:30 |
Tokyo | Tokyo Institute of Technology, and Online (Primary: On-site, Secondary: Online) |
[Short Paper]
Performance Evaluation on Split Learning Assisted Multi-UAV System for Image Classification Task Sun Tingkai, Wang Xiaoyan (Ibaraki Univ.), Masahiro Umehira (Nanzan Univ.) SR2022-93 |
Due to its ease of deployment and high mobility, unmanned aerial vehicles (UAVs) have gained popularity for a variety of... [more] | SR2022-93 pp.44-46 |
| NS, ICM, CQ, NV (Joint) |
2022-11-24 10:20 |
Fukuoka | Humanities and Social Sciences Center, Fukuoka Univ. + Online (Primary: On-site, Secondary: Online) |
Social Surplus Maximization Using Incentive Mechanism for Cross-Silo Federated Learning with Differential Privacy Shota Miyagoshi, Takuji Tachibana (Univ. Fukui) NS2022-101 |
In cross-silo federated learning, where multiple organizations participate, the prediction accuracy of the global model ... [more] | NS2022-101 pp.7-12 |
| NS, ICM, CQ, NV (Joint) |
2022-11-24 15:45 |
Fukuoka | Humanities and Social Sciences Center, Fukuoka Univ. + Online (Primary: On-site, Secondary: Online) |
A Study on Distributed Machine Learning with Rich Devices Feedbacking Results to Edge Servers Saki Takano (Ochanomizu Univ.), Akihiro Nakao (The Univ. of Tokyo), Saneyasu Yamaguchi (Kogakuin Univ.), Masato Oguchi (Ochanomizu Univ.) NS2022-111 |
Many recent studies have focused on using the data collected by edge devices for machine learning by aggregating those d... [more] | NS2022-111 pp.65-70 |
| NS, SR, RCS, SeMI, RCC (Joint) |
2022-07-14 14:25 |
Ishikawa | The Kanazawa Theatre + Online (Primary: On-site, Secondary: Online) |
Development and Evaluation of Blockchain-based Work Execution Status Management and Verification System Rui Tanaka (Ritsumeikan Univ.), Hiroshi Yamamoto (Ritsumeikan University) NS2022-48 |
In Japan, due to the revision of the model employment regulations, the companies that allows side jobs are increasing. H... [more] | NS2022-48 pp.112-117 |
| ICM, IPSJ-CSEC, IPSJ-IOT | 2022-05-20 14:15 |
Nagano | (Primary: On-site, Secondary: Online) |
Anomaly Event Classification Method using Observability Data in Autonomous Control Loop Yukitsugu Sasaki, Masaru Sakai, Kensuke Takahashi, Satoshi Kondou (NTT) ICM2022-9 |
An autonomous control loop system has been proposed in which each operation part operates autonomously by making the fun... [more] | ICM2022-9 pp.42-46 |
| NS, IN (Joint) |
2022-03-10 11:00 |
Online | Online | Experimental Evaluation of Influence of Distributing Deep Learning-Based IDSs on Their Classification Accuracy and Explainability Ayaka Oki, Yukio Ogawa, Kaoru Ota, Mianxiong Dong (Muroran-IT) IN2021-33 |
Increased data traffic associated with the wide spread usage of IoT devices accentuates the risk of large-scale cyber at... [more] | IN2021-33 pp.13-18 |
| SeMI | 2022-01-20 15:10 |
Nagano | (Primary: On-site, Secondary: Online) |
[Short Paper]
A Study of Joint Control of Machine Learning Model and Wireless LAN Parameters in Split inference by Reinforcement Learning Kojin Yorita (Tokyo Tech.), Sohei Itahara (Kyoto Univ.), Takayuki Nishio (Tokyo Tech.), Daiki Yoda, Toshihisa Nabetani (Toshiba) SeMI2021-66 |
Distributed inference (DI) enables machine learning (ML) inference with a deep neural network on resource-constrained de... [more] | SeMI2021-66 pp.51-54 |
| SeMI | 2022-01-21 15:20 |
Nagano | (Primary: On-site, Secondary: Online) |
[Short Paper]
Asynchronous Gradient-Boosted Decision Trees for Distributed Sensing Devices Yui Yamashita, Akihito Taya, Yoshito Tobe (Aoyama Gakuin Univ.) SeMI2021-64 |
Recently, wearable devices that install multiple sensors have been widely used. Although sensor data from these devices ... [more] | SeMI2021-64 pp.45-47 |
| SeMI | 2022-01-21 15:30 |
Nagano | (Primary: On-site, Secondary: Online) |
[Short Paper]
NLN: Name-based Learning Network Towards Efficient Distributed Machine Learning Tomoki Hirayama, Li Ruidong (Kanazawa Univ.) SeMI2021-58 |
With the increase in network traffic and the number of connected devices, future networks have recently been investigate... [more] | SeMI2021-58 pp.26-29 |
| RISING (3rd) |
2021-11-17 09:00 |
Tokyo | (Primary: On-site, Secondary: Online) |
Proposal of Incentive Mechanism for Cross-Silo Federated Learning with Differential Privacy Shota Miyagoshi, Takuji Tachibana (Univ. Fukui) |
In cross-silo federated learning, where multiple companies/organizations participate, the prediction accuracy of the glo... [more] | |
| NS | 2021-10-08 09:15 |
Online | Online | A study of privacy-preserving distributed machine learning using Rich Clients Saki Takano (Ochanomizu Univ.), Akihiro Nakao (The Univ. of Tokyo), Saneyasu Yamaguchi (Kogakuin Univ.), Masato Oguchi (Ochanomizu Univ.) NS2021-76 |
In recent years, edge computing has attracted much attention because of its advantages such as low latency and the abili... [more] | NS2021-76 pp.45-50 |
| CQ, MIKA (Joint) |
2021-09-10 12:40 |
Online | Online | [Special Invited Talk]
Toward Communication Efficient Federated Learning Takayuki Nishio (TokyoTech) CQ2021-56 |
Federated Learning (FL) is a machine learning framework that trains models using distributed data. Since FL can utilize ... [more] | CQ2021-56 pp.94-96 |
| 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 |
| SR | 2021-05-21 10:50 |
Online | Online | Performance Evaluation of Distributed Channel Selection Algorithm Based on Reinforcement Learning for Massive Mobile IoT Systems Daisuke Yamamoto, Honami Furukawa, Yusuke Ito, Aohan Li (TUS), Song-Ju Kim (Keio Univ.), Mikio Hasegawa (TUS) SR2021-11 |
In a Massive IoT environment, degradation of communication quality due to network congestion is a serious problem. In pr... [more] | SR2021-11 pp.73-78 |
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