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
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