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.
@article{arxiv.2602.19360,
title = {Compliance Management for Federated Data Processing},
author = {Natallia Kokash and Adam Belloum and Paola Grosso},
journal= {arXiv preprint arXiv:2602.19360},
year = {2026}
}