mlsanityData to 14 Oct 2026

Conformal Prediction for Federated Uncertainty Quantification Under Label Shift

arXiv 2306.05131 ↗First posted 8 Jun 2023stat.ML cs.LGcited by 29

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Abstract (arXiv)

Federated Learning (FL) is a machine learning framework where many clients collaboratively train models while keeping the training data decentralized. Despite recent advances in FL, the uncertainty quantification topic (UQ) remains partially addressed. Among UQ methods, conformal prediction (CP) approaches provides distribution-free guarantees under minimal assumptions. We develop a new federated conformal prediction method based on quantile regression and take into account privacy constraints. This method takes advantage of importance weighting to effectively address the label shift between agents and provides theoretical guarantees for both valid coverage of the prediction sets and differential privacy. Extensive experimental studies demonstrate that this method outperforms current competitors.

Key facts. Cited by 29 papers in the map (data through 14 Oct 2026). Builds on 32 of its 53 references that are in the map. First posted on arXiv 8 Jun 2023.

Builds on

32 of 53 references.

References · 32

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2019Advances and Open Problems in Federated Learningcited by 3,046
  4. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  5. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  6. 2019Towards Federated Learning at Scale: System Designcited by 832
  7. 2017Renyi Differential Privacycited by 650
  8. 2014Convex Optimization: Algorithms and Complexitycited by 575
  9. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  10. 2019Conformal Prediction Under Covariate Shiftcited by 519
  11. 2020Classification with Valid and Adaptive Coveragecited by 396
  12. 2021Adaptive Conformal Inference Under Distribution Shiftcited by 375
20 more
  1. 2018Detecting and Correcting for Label Shift with Black Box Predictorscited by 366
  2. 2020Uncertainty Sets for Image Classifiers using Conformal Predictioncited by 366
  3. 2022Conformal prediction beyond exchangeabilitycited by 355
  4. 2013The Composition Theorem for Differential Privacycited by 297
  5. 2018Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergencescited by 242
  6. 2020Conformal Inference of Counterfactuals and Individual Treatment Effectscited by 151
  7. 2020Minibatch vs Local SGD for Heterogeneous Distributed Learningcited by 147
  8. 2020The Discrete Gaussian for Differential Privacycited by 135
  9. 2020A Unified View of Label Shift Estimationcited by 119
  10. 2020Conformal Prediction: a Unified Review of Theory and New Challengescited by 118
  11. 2021Distribution-free uncertainty quantification for classification under label shiftcited by 101
  12. 2020Robust Validation: Confident Predictions Even When Distributions Shiftcited by 82
  13. 2021Differentially Private Federated Learning on Heterogeneous Datacited by 74
  14. 2019Federated Learning with Bayesian Differential Privacycited by 60
  15. 2021Tracking the risk of a deployed model and detecting harmful distribution shiftscited by 40
  16. 2021Differentially Private Quantilescited by 23
  17. 2021Private Prediction Setscited by 17
  18. 2021Distribution-Free Federated Learning with Conformal Predictionscited by 16
  19. 2022Recommendation Systems with Distribution-Free Reliability Guaranteescited by 13
  20. 2022Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantizationcited by 11

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 10; 2025: 5; 2026: 11.

29 papers.

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  1. 2023Conformal Prediction for Deep Classifier via Label Rankingcited by 53
  2. 2025Wasserstein-regularized Conformal Prediction under General Distribution Shiftcited by 15
  3. 2025Uncertainty Quantification for Machine Learning in Healthcare: A Surveycited by 14
  4. 2024Multi-Source Conformal Inference Under Distribution Shiftcited by 11
  5. 2023Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Predictioncited by 10
  6. 2023Efficient Conformal Prediction under Data Heterogeneitycited by 9
  7. 2024Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimationcited by 9
  8. 2024Certifiably Byzantine-Robust Federated Conformal Predictioncited by 8
  9. 2024FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scalercited by 6
  10. 2025Distributed Conformal Prediction via Message Passingcited by 4
  11. 2026Efficient Federated Conformal Prediction with Group-Conditional Guaranteecited by 4
  12. 2024Marginal and training-conditional guarantees in one-shot federated conformal predictioncited by 3
17 more
  1. 2024Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Reviewcited by 2
  2. 2025Federated Conditional Conformal Prediction via Generative Modelscited by 2
  3. 2026Conformalized Neural Networks for Federated Uncertainty Quantification under Dual Heterogeneitycited by 2
  4. 2026Certified Adaptive Refresh: Anytime-Valid Monitoring for Federated Conformal RAGcited by 2
  5. 2024Conditional Shift-Robust Conformal Prediction for Graph Neural Networkcited by 1
  6. 2026Multi-Agent Conformal Prediction with Personalized Statistical Validitycited by 1
  7. 2024Distribution-Free Fair Federated Learning with Small Samplescited by 0
  8. 2024RoCP-GNN: Robust Conformal Prediction for Graph Neural Networks in Node-Classificationcited by 0
  9. 2024Theory-inspired Label Shift Adaptation via Aligned Distribution Mixturecited by 0
  10. 2025Cost-Sensitive Conformal Training with Provably Controllable Learning Boundscited by 0
  11. 2026Communication-Efficient Byzantine-Robust Federated Conformal Prediction via Partial Sharingcited by 0
  12. 2026Softmax is not Enough (for Adaptive Conformal Classification)cited by 0
  13. 2026TrustFed: Enabling Trustworthy Medical AI under Data Privacy Constraintscited by 0
  14. 2026Continual Calibration: Coverage Can Collapse Before Accuracy in Lifelong LLM Fine-Tuningcited by 0
  15. 2026When Average Calibration Fails: Site-Conditional Federated Conformal Risk Controlcited by 0
  16. 2026Private Anytime Selective-Risk Certification for Federated Retrieval-Augmented Generation: Guarantees and Empirical Limitscited by 0
  17. 2026From matrix inversion to constraints: provably tighter confidence regions for importance weights in label shiftcited by 0