Compliance Management for Federated Data Processing

2026-02-22

AI Summary

This work addresses the compliance challenges in federated data processing arising from heterogeneous cross-organizational access policies, regulatory discrepancies, and long-running workflows. To tackle these issues, the paper proposes a compliance-aware federated data processing framework that uniquely integrates large language models (LLMs) with a “policy-as-code” approach. This integration enables the automatic translation of natural language descriptions of legal and organizational compliance requirements into executable machine-interpretable policies. An orchestration engine then enforces these policies dynamically across end-to-end workflows. Evaluation of the prototype system demonstrates that the proposed method effectively harmonizes multi-source compliance rules, significantly enhancing both compliance assurance and deployment feasibility in federated environments.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Federated Data Processing
• Compliance Management
• Access Policies
• Regulatory Requirements
• Workflow Orchestration

Innovation

• compliance-aware federated data processing
• policy-as-code
• workflow orchestration
• LLM-assisted compliance
• machine-actionable policies