1 citations · 1 across the 5 of their papers we have counts for
5 papers
IEMAS: An Incentive-Efficiency Routing Framework for Open Agentic Web Ecosystems
Hongze Liu, Chang Guo, Yingzeng Li +6
The transition to open, distributed Multi-Agent Systems (MAS) promises scalable intelligence but introduces a non-trivial tension: maximizing global efficiency requires cooperative…
BLOCKS: Blockchain-supported Cross-Silo Knowledge Sharing for Efficient LLM Services
Zhaojiacheng Zhou, Hongze Liu, Shijing Yuan +4
The hallucination problem of Large Language Models (LLMs) has increasingly drawn attention. Augmenting LLMs with external knowledge is a promising solution to address this issue. H…
Efficient Serverless Function Scheduling at the Network Edge
Jiong Lou, Zhiqing Tang, Shijing Yuan +3
Serverless computing is a promising approach for edge computing since its inherent features, e.g., lightweight virtualization, rapid scalability, and economic efficiency. However,…
Adaptive incentive for cross-silo federated learning: A multi-agent reinforcement learning approach
Shijing Yuan, Hongze Liu, Hongtao Lv +4
Cross-silo federated learning (FL) is a typical FL that enables organizations(e.g., financial or medical entities) to train global models on isolated data. Reasonable incentive is…
A Smart Contract based Crowdfunding Mechanism for Hierarchical Federated Learning
Hongze Liu, Jie Li, Shijing Yuan +2
Hierarchical Federated Learning (HFL) is introduced as a promising technique that allows model owners to fully exploit computational resources and bandwidth resources to train the…