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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| ITE-ME, ITE-IST, BioX, SIP, MI, IE [detail] | 2024-06-06 13:20 |
Niigata | Nigata University (Ekinan-Campus "TOKIMATE") | Enhanced Security with Random Binary Weights for Privacy-Preserving Federated Learning Hiroto Sawada, Shoko Imaizumi (Chiba Univ.), Hitoshi Kiya (TMU) |
(To be available after the conference date) [more] | |
| NLP | 2024-05-10 10:30 |
Kagawa | Kagawa Prefecture Social Welfare Center | Federated Learning Algorithms based on Decentralized Spanning Tree Generation and Step-by-Step Consensus Yuki Mori, Tatsuya Kayatani, Tsuyoshi Migita, Norikazu Takahashi (Okayama Univ.)  NLP2024-11 |
(To be available after the conference date) [more] | NLP2024-11 pp.52-57 |
| RCC, ISEC, IT, WBS | 2024-03-13 15:05 |
Osaka | Osaka Univ. (Suita Campus) | Efficient Replay Data Selection in Continual Federated Learning Model Yuto Kitano (Kobe Univ), Lihua Wang (NICT), Seiichi Ozawa (Kobe Univ) IT2023-96 ISEC2023-95 WBS2023-84 RCC2023-78 |
In this study, we propose a continual federated learning that can continuously learn distributed data generated daily by... [more] | IT2023-96 ISEC2023-95 WBS2023-84 RCC2023-78 pp.135-141 |
| PRMU, IBISML, IPSJ-CVIM | 2024-03-04 11:10 |
Hiroshima | Hiroshima Univ. Higashi-Hiroshima campus (Primary: On-site, Secondary: Online) |
Towards Client-aware Clustering Federated Learning based on Representations of Local Models Tatsuya Kaneko, Shinya Takamaeda-Yamazaki (Tokyo Univ.) IBISML2023-49 |
In the current era of rapidly expanding machine learning, there has been growing concerns and awareness of data privacy ... [more] | IBISML2023-49 pp.65-70 |
| SIP, SP, EA, IPSJ-SLP [detail] | 2024-02-29 17:20 |
Okinawa | (Primary: On-site, Secondary: Online) |
An Enhanced Privacy-Preserving Scheme for Federated Learning of Vision Transformer without Model Performance Degradation Rei Aso, Sayaka Shiota, Hitoshi Kiya (Tokyo Metropolitan Univ.) EA2023-80 SIP2023-127 SP2023-62 |
Federated learning is a learning method for training models over multiple participants without directly sharing their ra... [more] | EA2023-80 SIP2023-127 SP2023-62 pp.115-120 |
| SIP, SP, EA, IPSJ-SLP [detail] | 2024-02-29 15:10 |
Okinawa | (Primary: On-site, Secondary: Online) |
Byzantine attack detection via similarity of local updates in federated learning Kenta Ohno, Masao Yamagishi (Hosei Univ.) EA2023-86 SIP2023-133 SP2023-68 |
We propose a method to detect Byzantine attacks in federated learning, as well as a method for identifying clients repea... [more] | EA2023-86 SIP2023-133 SP2023-68 pp.150-155 |
| NS, IN (Joint) |
2024-02-29 09:45 |
Okinawa | Okinawa Convention Center | Communication cost and performance evaluation of each learning method in Federated learning with LLM Takumi Fukami, Yusuke Yamasaki, Iifan Tyou (NTT) IN2023-66 |
In recent years, a large amount of diverse data have been generated by various devices and organisations, and there has ... [more] | IN2023-66 pp.7-12 |
| NS, IN (Joint) |
2024-02-29 10:10 |
Okinawa | Okinawa Convention Center | Model Shifting Method in Federated Learning Using Distillation Hiromichi Yajima (SIT), Shota Ono (The Univ. of Tokyo), Takumi Miyoshi, Taku Yamazaki (SIT) NS2023-186 |
Due to the drastic increase in the data for machine learning, distributed machine learning such as federated learning ha... [more] | NS2023-186 pp.86-89 |
| NS, IN (Joint) |
2024-02-29 10:45 |
Okinawa | Okinawa Convention Center | Proposal of a Data Leakage Attack against a Vertical Federated Learning System based on Knowledge Distillation Takumi Suimon, Yuki Koizumi, Junji Takemasa, Toru Hasegawa (Osaka Univ.) NS2023-187 |
Vertical federated learning is a method for participants who have data with the same samples but different features to c... [more] | NS2023-187 pp.90-95 |
| NS, IN (Joint) |
2024-02-29 09:20 |
Okinawa | Okinawa Convention Center | Intrusion Detection System Based on Federated Decision Trees Naoto Watanabe, Taku Yamazaki, Takumi Miyoshi (Shibaura Inst. Tech.), Masataka Nakahara, Norihiro Okui, Ayumu Kubota (KDDI Research) NS2023-190 |
With the proliferation of Internet of things (IoT) devices, cyberattacks targeting these devices have also been increasi... [more] | NS2023-190 pp.109-112 |
| EMM | 2024-01-16 15:25 |
Miyagi | Tohoku Univ. (Primary: On-site, Secondary: Online) |
[Invited Talk]
Federated Learning with Enhanced Privacy Protection in AI Lihua Wang (NICT) EMM2023-83 |
Federated learning is a crucial methodology in artificial intelligence where multiple organizations collaborate to perfo... [more] | EMM2023-83 p.19 |
| SRW, SeMI (Joint) |
2023-11-21 17:00 |
Tokyo | Koganei Campus, Tokyo University of Agriculture and Technology (Primary: On-site, Secondary: Online) |
[Poster Presentation]
A coaching system for improving running form with considering privacy protection Kyoshiro Takanashi, Norihiko Shinomiya (Soka Univ) SeMI2023-45 |
In recent years, there has been a substantial rise in the population of marathon runners. A lot of runners ’motivation i... [more] | SeMI2023-45 pp.23-26 |
| CS | 2023-11-10 09:40 |
Shizuoka | Plaza Verde | [Invited Lecture]
An AI Platform for Smart City Digital Twins Koji Zettsu (NICT) CS2023-74 |
In recent years, extensive researches and developments have been made to collect, monitor, and manage urban data to faci... [more] | CS2023-74 pp.42-46 |
| SR | 2023-11-10 10:55 |
Miyagi | (Primary: On-site, Secondary: Online) |
[Short Paper]
On Model Transfer with Deep Joint Source Channel Coding Katsuya Suto, Issa Matsumura, Junichiro Yamada (UEC) SR2023-58 |
Based on the source channel separation theorem, the current multimedia transfer system employs independently designed so... [more] | SR2023-58 pp.61-63 |
| RISING (3rd) |
2023-10-31 10:45 |
Hokkaido | Kaderu 2・7 (Sapporo) | [Poster Presentation]
Experiments on a Large-scale Federated Learning System for Fishing Prediction Harii Oura, Takuji Tachibana, Tomoya Kawakami (Fukui Univ) |
In federated learning, a method of distributed learning, a global model is constructed from only the training results wi... [more] | |
| RISING (3rd) |
2023-10-31 10:45 |
Hokkaido | Kaderu 2・7 (Sapporo) | [Poster Presentation]
Formulation of a Social Surplus Optimization Problem for Predicting Fishing Outcomes Using Federated Learning Shotaro Kitano, Shota Miyagoshi, Takuji Tachibana (Univ. Fukui) |
In cross-device federative learning, where each client participates with an IoT device such as a smartphone, a large num... [more] | |
| RISING (3rd) |
2023-10-31 10:45 |
Hokkaido | Kaderu 2・7 (Sapporo) | [Poster Presentation]
Experimental Evaluation of Fishing Prediction Using Federation Learning Yutaka Hatazawa, Shota Miyagosi, Tomoya Kawakami, Takuji Tachibana (Univ. Fukui) |
In order to revitalize the recreational fishing industry in rivers, efforts are underway to make fishing tickets availab... [more] | |
| IN, ICTSSL, IEE-SMF | 2023-10-19 09:25 |
Fukuoka | Fukuoka University | An onboard vehicle camera object tracking for data labeling of object detection by Federated Learning Yuki Nakahama, Satoshi Ohzahata, Ryo Yamamoto (UEC) IN2023-33 |
In recent years, auto vehicle driving technology has made significant advancements. An achieving fully automatic driving... [more] | IN2023-33 pp.5-10 |
| BioX | 2023-10-12 14:45 |
Okinawa | Nobumoto Ohama Memorial Hall | A Study on Federated Learning System for Highly Accurate Biometric Authentication Yusei Suzuki, Yosuke Kaga (Hitachi) BioX2023-58 |
With the development of machine learning on large-scale datasets, the accuracy of biometric authentication has significa... [more] | BioX2023-58 pp.2-7 |
| 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 |
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