1 citations · 1 across the 7 of their papers we have counts for
10 papers
Tram-FL: Reducing Communication and Computation Costs through Sequential Model Circulation in Decentralized Federated Learning
Kota Maejima, Takayuki Nishio, Asato Yamazaki +1
Conventional decentralized federated learning (DFL) often focuses on clients, with each client maintaining a model copy, performing updates individually, and undertaking model exch…
Robust Unsupervised Network Intrusion Detection via Federated Learning with Selective Aggregation under Anomalous Sample Contamination
Shohei Kamiguchi, Takayuki Nishio
Network intrusion detection systems (NIDS) have become essential for Internet of Things (IoT) environments, as malware targeting IoT devices continues to evolve in sophistication.…
Enabling Federated Inference via Unsupervised Consensus Embedding
Yui Hashimoto, Takayuki Nishio, Yuichi Kitagawa +1
Cooperative inference across independently deployed machine learning models is increasingly desirable in distributed environments, as there is a growing need to leverage multiple m…
BlazeFL: Fast and Deterministic Federated Learning Simulation
Kitsuya Azuma, Takayuki Nishio
Federated learning (FL) research increasingly relies on single-node simulations with hundreds or thousands of virtual clients, making both efficiency and reproducibility essential.…
Multi-Station WiFi CSI Sensing Framework Robust to Station-wise Feature Missingness and Limited Labeled Data
Keita Kayano, Takayuki Nishio, Daiki Yoda +2
We propose a WiFi Channel State Information (CSI) sensing framework for multi-station deployments that addresses two fundamental challenges in practical CSI sensing: station-wise f…
Lightweight User-Personalization Method for Closed Split Computing
Yuya Okada, Takayuki Nishio
Split Computing enables collaborative inference between edge devices and the cloud by partitioning a deep neural network into an edge-side head and a server-side tail, reducing lat…
SALT: A Lightweight Model Adaptation Method for Closed Split Computing Environments
Yuya Okada, Takayuki Nishio
We propose SALT (Split-Adaptive Lightweight Tuning), a lightweight model adaptation framework for Split Computing under closed constraints, where the head and tail networks are pro…
Load-Aware Training Scheduling for Model Circulation-based Decentralized Federated Learning
Haruki Kainuma, Takayuki Nishio
This paper proposes Load-aware Tram-FL, an extension of Tram-FL that introduces a training scheduling mechanism to minimize total training time in decentralized federated learning…
High-resolution efficient image generation from WiFi CSI using a pretrained latent diffusion model
Eshan Ramesh, Takayuki Nishio
We present LatentCSI, a novel method for generating images of the physical environment from WiFi CSI measurements that leverages a pretrained latent diffusion model (LDM). Unlike p…
Soft-Label Caching and Sharpening for Communication-Efficient Federated Distillation
Kitsuya Azuma, Takayuki Nishio, Yuichi Kitagawa +2
Federated Learning (FL) enables collaborative model training across decentralized clients, enhancing privacy by keeping data local. Yet conventional FL, relying on frequent paramet…