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arXiv · 2602.19360

Compliance Management for Federated Data Processing

Natallia Kokash·Adam Belloum·Paola Grosso
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Abstract

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.

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Natallia Kokash, Adam Belloum, Paola Grosso. 2026-02-22. Compliance Management for Federated Data Processing. https://arxiv.org/abs/2602.19360

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