Federated data processing (FDP) offers a promising approach for enabling collaborative analysis of sensitive data without centralizing raw datasets. However, real-world adoption remains limited due to the complexity of managing heterogeneous access policies, regulatory requirements, and long-running workflows across organizational boundaries. In this paper, we present a framework for compliance-aware FDP that integrates policy-as-code, workflow orchestration, and large language model (LLM)-assisted compliance management. Through the implemented prototype, we show how legal and organizational requirements can be collected and translated into machine-actionable policies in FDP networks.

翻译：联邦数据处理（FDP）为敏感数据的协作分析提供了一种有前景的途径，无需集中原始数据集。然而，由于管理异构访问策略、监管要求以及跨组织边界的长期运行工作流的复杂性，其在现实世界的应用仍然有限。本文提出了一种合规感知的FDP框架，该框架集成了策略即代码、工作流编排以及大型语言模型（LLM）辅助的合规管理。通过实现的原型系统，我们展示了如何在FDP网络中收集法律与组织要求，并将其转化为机器可执行的策略。