Loading
MPSoC 2022
  • Home
  • Commitees
  • Agenda
  • Speakers
  • Registration
  • Venue & Hotel
  • MPSoC Book
  • Menu

Hiroki Matsutani

Keio University, Japan

An On-Device Learning Approach and Its Extension for Federated Learning

Abstract

Most edge AI focuses on prediction tasks on resource-limited edge devices, while the training is done at server machines, so retraining a model on the edge devices to reflect environmental changes is a complicated task. To follow such environmental changes, a neural-network based on-device learning approach is recently proposed, so that edge devices can train incoming data at runtime to update their model. However, since a training is done at distributed edge devices, the issue is that only a limited amount of training data can be used for each edge device. To address this issue, this talk presents an on-device federated learning for cooperative model update, where edge devices exchange their intermediate trained results and update their model by using those collected from the other edge devices.

Biography

Hiroki Matsutani received the BA, ME, and PhD degrees from Keio University, Yokohama, Japan, in 2004, 2006, and 2008, respectively. He is currently an Associate Professor in the Department of Information and Computer Science, Keio University. His research interests are related to computer architecture, interconnection networks, hardware accelerators, and machine learning algorithms.

If you wish to modify any information or update your photo, please contact the Publicity Chair at the following address:
arief.wicaksana[at]cea.fr

Contact

Please address any issue to web admin arief.wicaksana@cea.fr

Active Pages

  • Agenda
  • Commitees
  • MPSoC Book
  • Registration
  • Speakers
  • Venue & Hotel

Categories

  • Uncategorized
© Copyright - MPSoC 2020 | Privacy Policy
Youn-Long Lin Ittetsu Taniguchi
Scroll to top