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CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learning

Created by
  • Haebom

Author

Kaveen Hiniduma, Zilinghan Li, Aditya Sinha, Ravi Madduri, Suren Byna

Outline

This paper presents CADRE (Customizable Assurance of Data Readiness), a novel framework for ensuring data readiness (DR) in Privacy-Preserving Federated Learning (PPFL), a distributed machine learning technique that guarantees privacy. CADRE allows users to define custom DR metrics, rules, and solutions tailored to specific federated learning tasks. Based on the custom metrics, rules, and solutions, it generates comprehensive DR reports to ensure dataset readiness for FL while preserving privacy. Experiments demonstrate the versatility and effectiveness of CADRE, ensuring DR across various dimensions, including data quality, privacy, and fairness. We demonstrate real-world applications by integrating CADRE into the existing PPFL framework, addressing six datasets and seven DR problems.

Takeaways, Limitations

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Takeaways:
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It provides customizable data readiness metrics, rules, and solutions, providing flexibility for applying to a variety of federated learning tasks.
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Improve the performance and reliability of federated learning models by verifying and improving data readiness while ensuring privacy.
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We help you systematically manage and resolve data readiness issues to efficiently utilize valuable resources.
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We validate the practicality and effectiveness of CADRE through experimental results on various datasets and DR problems.
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Limitations:
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Further research is needed to determine the generalizability of the proposed DR metrics, rules, and solutions.
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Extensive experimentation with diverse federated learning environments and data types is required.
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Further evaluation of the scalability and performance of the CADRE framework is needed.
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