Search Results for author: Yusuke Koda

Found 9 papers, 0 papers with code

Communication-oriented Model Fine-tuning for Packet-loss Resilient Distributed Inference under Highly Lossy IoT Networks

no code implementations17 Dec 2021 Sohei Itahara, Takayuki Nishio, Yusuke Koda, Koji Yamamoto

However, generally, there is a communication system-level trade-off between communication latency and reliability; thus, to provide accurate DI results, a reliable and high-latency communication system is required to be adapted, which results in non-negligible end-to-end latency of the DI.

Frame-Capture-Based CSI Recomposition Pertaining to Firmware-Agnostic WiFi Sensing

no code implementations29 Oct 2021 Ryosuke Hanahara, Sohei Itahara, Kota Yamashita, Yusuke Koda, Akihito Taya, Takayuki Nishio, Koji Yamamoto

This indicates that WiFi sensing that leverages the BFM matrix is more practical to implement using the pre-installed APs.

Beamforming Feedback-based Model-Driven Angle of Departure Estimation Toward Legacy Support in WiFi Sensing: An Experimental Study

no code implementations27 Oct 2021 Sohei Itahara, Sota Kondo, Kota Yamashita, Takayuki Nishio, Koji Yamamoto, Yusuke Koda

Moreover, the evaluations performed in this study revealed that the BFF-based MUSIC achieved a comparable error of AoD estimation to the CSI-based MUSIC, while BFF is a highly compressed version of CSI in IEEE 802. 11ac/ax.

AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning

no code implementations2 May 2021 Yusuke Koda, Jihong Park, Mehdi Bennis, Praneeth Vepakomma, Ramesh Raskar

In AirMixML, multiple workers transmit analog-modulated signals of their private data samples to an edge server who trains an ML model using the received noisy-and superpositioned samples.

BIG-bench Machine Learning Data Augmentation +1

Zero-Shot Adaptation for mmWave Beam-Tracking on Overhead Messenger Wires through Robust Adversarial Reinforcement Learning

no code implementations16 Feb 2021 Masao Shinzaki, Yusuke Koda, Koji Yamamoto, Takayuki Nishio, Masahiro Morikura, Yushi Shirato, Daisei Uchida, Naoki Kita

Second, we demonstrate the feasibility of \textit{zero-shot adaptation} as a solution, where a learning agent adapts to environmental parameters unseen during training.

When Wireless Communications Meet Computer Vision in Beyond 5G

no code implementations13 Oct 2020 Takayuki Nishio, Yusuke Koda, Jihong Park, Mehdi Bennis, Klaus Doppler

This article articulates the emerging paradigm, sitting at the confluence of computer vision and wireless communication, to enable beyond-5G/6G mission-critical applications (autonomous/remote-controlled vehicles, visuo-haptic VR, and other cyber-physical applications).

Image Reconstruction

Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Data

no code implementations14 Aug 2020 Sohei Itahara, Takayuki Nishio, Yusuke Koda, Masahiro Morikura, Koji Yamamoto

To this end, based on the idea of leveraging an unlabeled open dataset, we propose a distillation-based semi-supervised FL (DS-FL) algorithm that exchanges the outputs of local models among mobile devices, instead of model parameter exchange employed by the typical frameworks.

Data Augmentation Federated Learning

Differentially Private AirComp Federated Learning with Power Adaptation Harnessing Receiver Noise

no code implementations14 Apr 2020 Yusuke Koda, Koji Yamamoto, Takayuki Nishio, Masahiro Morikura

To this end, a differentially private AirComp-based FL is designed in this study, where the key idea is to harness receiver noise perturbation injected to aggregated global models inherently, thereby preventing the inference of clients' private data.

Networking and Internet Architecture Signal Processing

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