国際会議 · 2026

High-Reliability Network-Distributed MoE-LLM System

Haruki Yonekura , Hirozumi Yamaguchi

ICDCN 2026 Doctoral Symposium

DOI: 10.1145/3737611.3776945

Abstract

For the practical deployment of distributed Mixture-of-Experts large language models(MoE-LLM), ensuring resilience is as critical as achieving computational efficiency. This research tackles this pressing challenge by introducing a resilient distributed LLM architecture that dynamically maintains system reliability in large-scale networked environments. The proposed framework develops cooperative routing mechanisms that adaptively account for the reliability of individual Experts, as well as resistance mechanisms that mitigate cascade failures and suppress malicious Expert behaviors. Furthermore, it establishes a quantitative evaluation framework through the introduction of the Resilience Score, a comprehensive metric designed to assess system robustness under fault and attack conditions. Through these contributions, this study aims to provide foundational design principles for constructing trustworthy, fault-tolerant, and high-performance distributed LLM systems, paving the way for their safe and reliable integration into future cyber-physical infrastructures.

災害時LoRaネットワークのための環境認識型分散スケジューリング

Yuto Inaba, Tatsuya Amano, Akihito Hiromori, Hirozumi Yamaguchi

2026 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), SPT-IoT 2026, pp. 1366–1371

DOI 10.1109/PerComWorkshops68308.2026.11585469

災害通信LoRa +4

災害現場画像要約のための軽量Vision-Language Model

Hibiki Yoshizaki, Akira Uchiyama, Akihito Hiromori, Mineo Takai, Hirozumi Yamaguchi

2026 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), PerconAI 2026, pp. 1203–1208

DOI 10.1109/PerComWorkshops68308.2026.11585419

セマンティック通信災害対応 +4

物理モデル統合型深層学習による都市の土砂災害予測

Ren Ozeki, Hamada Rizk, Hirozumi Yamaguchi

2026 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), URBSENSE 2026, pp. 1094–1099

DOI 10.1109/PerComWorkshops68308.2026.11585337

土砂災害予測物理モデル統合学習 +3

超大規模衛星群の精密編隊飛行に向けたシミュレーションフレームワーク

Tatsuya Amano, Akihito Hiromori, Hirozumi Yamaguchi, Sumio Morioka

2026 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), PerVehicle , pp. 230–235

DOI 10.1109/PerComWorkshops68308.2026.11585321

衛星編隊飛行分散シミュレーション +4

レイトレーシング駆動型ISACレーダによるパターンベース車両認識

Heetae Jin, Akira Uchiyama

2026 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), PerRad 2026, pp. 328–333

DOI 10.1109/PerComWorkshops68308.2026.11585327

ISACBeyond 5G +4

A Questionnaire-Only Counterfactual Machine Learning Approach to Assess the Spatial Impact of Green Mobility Vehicles in Urban Parks

Rami Naeem, Srikant Manas, Tatsuya Amano, Hirozumi Yamaguchi

ICDCN 2026 Workshop: IWNDSC2026

DOI 10.1145/3737611.3776620