Compliance Management for Federated Data Processing

• URL: http://arxiv.org/abs/2602.19360v1
• Date: Sun, 22 Feb 2026 22:10:25 GMT
• Title: Compliance Management for Federated Data Processing
• Authors: Natallia Kokash, Adam Belloum, Paola Grosso,
• Abstract summary: Federated data processing (FDP) offers a promising approach for enabling collaborative analysis of sensitive data without centralizing raw datasets.<n>We present a framework for compliance-aware FDP that integrates policy-as-code, workflow orchestration, and large language model (LLM)-assisted compliance management.
• Score: 1.3836910960262496
• License: http://creativecommons.org/licenses/by/4.0/
• 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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