mlsanityData to 14 Oct 2026

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

arXiv 2505.23849 ↗First posted 28 May 2025cs.CR cs.AI cs.LGcited by 0

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Abstract (arXiv)

Privacy-Preserving Federated Learning (PPFL) is a decentralized machine learning approach where multiple clients train a model collaboratively. PPFL preserves the privacy and security of a client's data without exchanging it. However, ensuring that data at each client is of high quality and ready for federated learning (FL) is a challenge due to restricted data access. In this paper, we introduce CADRE (Customizable Assurance of Data Readiness) for federated learning (FL), a novel framework that allows users to define custom data readiness (DR) metrics, rules, and remedies tailored to specific FL tasks. CADRE generates comprehensive DR reports based on the user-defined metrics, rules, and remedies to ensure datasets are prepared for FL while preserving privacy. We demonstrate a practical application of CADRE by integrating it into an existing PPFL framework. We conducted experiments across six datasets and addressed seven different DR issues. The results illustrate the versatility and effectiveness of CADRE in ensuring DR across various dimensions, including data quality, privacy, and fairness. This approach enhances the performance and reliability of FL models as well as utilizes valuable resources.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 48 references that are in the map. First posted on arXiv 28 May 2025.

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23 of 48 references.

References · 23

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  3. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  4. 2020FLTrust: Byzantine-robust Federated Learning via Trust Bootstrappingcited by 346
  5. 2021Federated Learning with Buffered Asynchronous Aggregationcited by 190
  6. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 125
  7. 2017HoloClean: Holistic Data Repairs with Probabilistic Inferencecited by 79
  8. 2021EIFFeL: Ensuring Integrity for Federated Learningcited by 28
  9. 2020Fairness and Accuracy in Federated Learningcited by 27
  10. 2022APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learningcited by 20
  11. 2024Advances in APPFL: A Comprehensive and Extensible Federated Learning Frameworkcited by 19
  12. 2021Data Quality Toolkit: Automatic assessment of data quality and remediation for machine learning datasetscited by 18
11 more
  1. 2020Data Readiness Reportcited by 17
  2. 2024Data Readiness for AI: A 360-Degree Surveycited by 15
  3. 2024FedMef: Towards Memory-efficient Federated Dynamic Pruningcited by 14
  4. 2023A Survey on Class Imbalance in Federated Learningcited by 13
  5. 2023FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Schedulercited by 13
  6. 2024Tackling Noisy Clients in Federated Learning with End-to-end Label Correctioncited by 12
  7. 2024AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AIcited by 9
  8. 2021An Experimental Study of Class Imbalance in Federated Learningcited by 7
  9. 2024Enhancing Data Quality in Federated Fine-Tuning of Foundation Modelscited by 4
  10. 2022LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximationcited by 3
  11. 2024Data Quality Control in Federated Instruction-tuning of Large Language Modelscited by 1

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