104 citations · 167 across the 14 of their papers we have counts for
24 papers
Tram-FL: Routing-based Model Training for Decentralized Federated Learning
Kota Maejima, Takayuki Nishio, Asato Yamazaki +1
In decentralized federated learning (DFL), substantial traffic from frequent inter-node communication and non-independent and identically distributed (non-IID) data challenges high…
Point Cloud-based Proactive Link Quality Prediction for Millimeter-wave Communications
Shoki Ohta, Takayuki Nishio, Riichi Kudo +2
This study demonstrates the feasibility of point cloud-based proactive link quality prediction for millimeter-wave (mmWave) communications. Previous studies have proposed machine l…
Watch from sky: machine-learning-based multi-UAV network for predictive police surveillance
Ryusei Sugano, Ryoichi Shinkuma, Takayuki Nishio +2
This paper presents the watch-from-sky framework, where multiple unmanned aerial vehicles (UAVs) play four roles, i.e., sensing, data forwarding, computing, and patrolling, for pre…
Packet-Loss-Tolerant Split Inference for Delay-Sensitive Deep Learning in Lossy Wireless Networks
Sohei Itahara, Takayuki Nishio, Koji Yamamoto
The distributed inference framework is an emerging technology for real-time applications empowered by cutting-edge deep machine learning (ML) on resource-constrained Internet of th…
Zero-Shot Adaptation for mmWave Beam-Tracking on Overhead Messenger Wires through Robust Adversarial Reinforcement Learning
Masao Shinzaki, Yusuke Koda, Koji Yamamoto +5
Millimeter wave (mmWave) beam-tracking based on machine learning enables the development of accurate tracking policies while obviating the need to periodically solve beam-optimizat…
Millimeter Wave Communications on Overhead Messenger Wire: Deep Reinforcement Learning-Based Predictive Beam Tracking
Yusuke Koda, Masao Shinzaki, Koji Yamamoto +5
This paper discusses the feasibility of beam tracking against dynamics in millimeter wave (mmWave) nodes placed on overhead messenger wires, including wind-forced perturbations and…