Cortex-FL：智能体中介的联邦学习协作系统
Cortex-FL: Agent-Mediated Collaboration for Federated Learning
AI 导读
arXiv 论文提出 Cortex-FL，一个智能体原生的联邦学习系统，用户可用自然语言表达学习目标并委托智能体完成协作。智能体负责将目标转化为共享任务、发现协作者并协商参与协议，架构上把智能体协调与本地执行权分离，数据所有者保留对数据、计算和资金的控制。作者在四节点部署上演示了完整流程，包括协作组建、合约治理的训练、验证后的模型交付和区块链测试网上的 x402 结算。
正文 · 原文
Abstract:Federated learning (FL) enables independent data owners to train models collaboratively without centralizing raw data. However, identifying collaborators, preparing training tasks, and coordinating execution and incentives hinder its practical adoption by organizations without specialized expertise. We present Cortex-FL, an agent-native FL system that enables users to express learning objectives in natural language and delegate the collaboration required to train a model. Agents translate these objectives into shared tasks, discover collaborators, and negotiate participation agreements, connecting collaboration formation with distributed training and incentive management. The architecture separates agent coordination from local execution authority, allowing owners to delegate coordination while retaining control over their data, computation, and financial commitments. We demonstrate the complete workflow on a four-node deployment, in which a model requested in natural language proceeds through collaboration formation, contract-governed training, verified model delivery, and x402 settlement on a blockchain testnet.
| Subjects: | Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2611.17186 [cs.AI] |
| (or arXiv:2611.17186v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.17186 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Rui Sun [view email]
[v1]
Thu, 12 Nov 2026 17:52:51 UTC (2,091 KB)
来源：arXiv cs.AI · arxiv.org