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

arXiv: https://arxiv.org/abs/2505.23849 · HTML: https://mlsanity.com/paper/2505.23849/

## 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.

## Builds on (most cited first, 23 of 23)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
- [FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping](https://mlsanity.com/paper/2012.13995/) (2020)
- [Federated Learning with Buffered Asynchronous Aggregation](https://mlsanity.com/paper/2106.06639/) (2021)
- [FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings](https://mlsanity.com/paper/2210.04620/) (2022)
- [HoloClean: Holistic Data Repairs with Probabilistic Inference](https://mlsanity.com/paper/1702.00820/) (2017)
- [EIFFeL: Ensuring Integrity for Federated Learning](https://mlsanity.com/paper/2112.12727/) (2021)
- [Fairness and Accuracy in Federated Learning](https://mlsanity.com/paper/2012.10069/) (2020)
- [APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning](https://mlsanity.com/paper/2202.03672/) (2022)
- [Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework](https://mlsanity.com/paper/2409.11585/) (2024)
- [Data Quality Toolkit: Automatic assessment of data quality and remediation for machine learning datasets](https://mlsanity.com/paper/2108.05935/) (2021)
- [Data Readiness Report](https://mlsanity.com/paper/2010.07213/) (2020)
- [Data Readiness for AI: A 360-Degree Survey](https://mlsanity.com/paper/2404.05779/) (2024)
- [FedMef: Towards Memory-efficient Federated Dynamic Pruning](https://mlsanity.com/paper/2403.14737/) (2024)
- [A Survey on Class Imbalance in Federated Learning](https://mlsanity.com/paper/2303.11673/) (2023)
- [FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Scheduler](https://mlsanity.com/paper/2309.14675/) (2023)
- [Tackling Noisy Clients in Federated Learning with End-to-end Label Correction](https://mlsanity.com/paper/2408.04301/) (2024)
- [AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI](https://mlsanity.com/paper/2406.19256/) (2024)
- [An Experimental Study of Class Imbalance in Federated Learning](https://mlsanity.com/paper/2109.04094/) (2021)
- [Enhancing Data Quality in Federated Fine-Tuning of Foundation Models](https://mlsanity.com/paper/2403.04529/) (2024)
- [LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation](https://mlsanity.com/paper/2205.11518/) (2022)
- [Data Quality Control in Federated Instruction-tuning of Large Language Models](https://mlsanity.com/paper/2410.11540/) (2024)

## 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.