Search Results for author: Hiroshi Esaki

Found 5 papers, 2 papers with code

Reinforcement Learning Based Optimal Camera Placement for Depth Observation of Indoor Scenes

no code implementations21 Oct 2021 Yichuan Chen, Manabu Tsukada, Hiroshi Esaki

The experimental results indicate that the proposed system outperforms seven out of ten test scenes in obtaining lower depth observation error.


Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness

no code implementations30 Jul 2021 Yuwei Sun, Hideya Ochiai, Hiroshi Esaki

Wider coverage and a better solution to a latency reduction in 5G necessitate its combination with multi-access edge computing (MEC) technology.

Distributed Computing Edge-computing +2

Intrusion Detection with Segmented Federated Learning for Large-Scale Multiple LANs

1 code implementation International Joint Conference on Neural Networks (IJCNN) 2020 Yuwei Sun, Hideya Ochiai, Hiroshi Esaki

In this research, a segmented federated learning is proposed, different from a collaborative learning based on single global model in a traditional federated learning model, it keeps multiple global models which allow each segment of participants to conduct collaborative learning separately and rearranges the segmentation of participants dynamically as well.

Network Intrusion Detection Personalized Federated Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.