AI 中文总结
针对跨筒仓联邦学习中会话准入的实际瓶颈问题，提出会话准入网关，通过组织批准生成令牌，运行时请求携带相关证明，将准入简化为加密验证和能力匹配，并用MNIST进行端到端验证，概念验证开源可重现。
AI 中文摘要
跨筒仓联邦学习将原始数据本地化，但在跨组织边界决定谁可以调用哪些会话范围操作时，部署常常因实际瓶颈而停滞，且执行后仍需可审计。实际中准入通过集中式策略服务等实现，存在问题。我们提出会话准入网关，在边界执行预批准会话能力并发出可验证决策记录。设置时组织批准角色等并生成令牌，运行时请求携带令牌和证明，准入简化为无状态加密验证和能力匹配。我们用MNIST在跨筒仓FL工作流程上进行了端到端验证，概念验证是开源、容器化且可重现的。
英文摘要
Cross-silo federated learning keeps raw data local, but deployments frequently stall on a practical bottleneck when deciding who may invoke which session-scoped operations across organizational boundaries, under constraints that remain auditable after execution. In practice, admission is implemented via centralized policy services, platform configuration, or ad hoc checks, which drift over time and are hard to audit from boundary-visible evidence. We present a session admission gateway that enforces pre-approved session capabilities at the boundary and emits verifiable decision records. During session setup, participating organizations approve roles and constraints and mint signed session capability tokens that enumerate permitted session operations for a given session_id. At runtime, each request carries the token and a request-bound proof-of-possession, making replay and impersonation with stolen tokens detectable at the gateway via request binding. Admission reduces to stateless per-request cryptographic verification and capability matching at an admission gateway, while setup and orchestration remain out of scope and organization-specific. We validate the approach end-to-end on a cross-silo FL workflow using MNIST as a surrogate workload. The proof-of-concept is open-source, containerized, provisioned as code, and includes reproducible tests and evidence logs.
CommentsAccepted at the 2nd International Conference on Federated Learning and Intelligent Computing Systems (FLICS2026)
