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论文精选 65arXiv

FedReady: Bounded Agents for Federation Formation· FedReady: 用于联邦组建的有限代理

Federated learning (FL) runtimes standardize aggregation and deployment, but each new study still requires federation formation: selecting task-eligible sites and preparing their local data and task code for federated training or analysis. This preparation recurs when the target changes, while raw records and site-private mappings are excluded from cross-site FL messages. We present FedReady, a system that performs federation formation with bounded LLM agents. Agents propose shared contracts, local adapters, and task code; the runtime owns workflow state and accepts artifacts only after checks tied to their digests. Candidate site discovery prioritizes recall; local eligibility verification checks task support before site admission. Once participating sites and artifacts are fixed, convent

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FedReady 通过有限代理简化联邦学习的组建过程

核心方法
使用有限的大型语言模型代理(LLM agents)来提出共享合约、本地适配器和任务代码,并通过运行时状态管理和验证来确保联邦学习的安全性和一致性
适合谁读
研究者 / 工程师
要解决的问题
联邦学习每次新研究都需要重新选择合适的站点并准备数据和代码,过程复杂且冗余
关键实验
未提供
主要贡献
引入 FedReady 系统,优化联邦学习的组建流程,减少重复工作,提高安全性
意义与局限
显著简化联邦学习的研究准备过程,提高跨站点联邦学习的效率和安全性,但依赖于代理的有效性和安全性
领域:cs.AI作者:Ziyue Xu、Holger R. Roth、Chester Chen
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论文精选 65已解读

Strategist: Evolving Reasoning with Agents· 演进推理策略的AI元代理

A model is only half of an AI agent system. The other half is the inference strategy: the …

领域:cs.AI作者:Taiming Lu、Qi Xu、Zhigang Hua
📎 arXiv🕒 11-03 02:51🔗 arxiv.org
论文精选 65已解读

DiabAgent: Toward Agentic Physiological Control· DiabAgent:迈向代理生理控制

As pretraining scales, medical language models have advanced rapidly in clinical question …

领域:cs.AI作者:Chengxi Li、Shaoxuan Li、Deyao Hong
📎 arXiv🕒 11-03 02:41🔗 arxiv.org

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