mlsanityData to 7 Oct 2026

One-Shot Federated Conformal Prediction

arXiv 2302.06322 ↗First posted 13 Feb 2023stat.MLcited by 26

Not on the map. Why?

Abstract (arXiv)

In this paper, we introduce a conformal prediction method to construct prediction sets in a oneshot federated learning setting. More specifically, we define a quantile-of-quantiles estimator and prove that for any distribution, it is possible to output prediction sets with desired coverage in only one round of communication. To mitigate privacy issues, we also describe a locally differentially private version of our estimator. Finally, over a wide range of experiments, we show that our method returns prediction sets with coverage and length very similar to those obtained in a centralized setting. Overall, these results demonstrate that our method is particularly well-suited to perform conformal predictions in a one-shot federated learning setting.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 28 of its 58 references that are in the map. First posted on arXiv 13 Feb 2023.

Builds on

28 of 58 references.

References · 28

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  4. 2021A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantificationcited by 777
  5. 2016Distribution-Free Predictive Inference For Regressioncited by 657
  6. 2019Conformalized Quantile Regressioncited by 620
  7. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
  8. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
  9. 2019Differentially Private Learning with Adaptive Clippingcited by 218
  10. 2019SCAFFOLD: Stochastic Controlled Averaging for Federated Learningcited by 174
  11. 2019One-Shot Federated Learningcited by 124
  12. 2020Conformal Prediction: a Unified Review of Theory and New Challengescited by 118
16 more
  1. 2020Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shufflingcited by 96
  2. 2019Nested conformal prediction and quantile out-of-bag ensemble methodscited by 88
  3. 2021Heterogeneity for the Win: One-Shot Federated Clusteringcited by 87
  4. 2021Conformal Prediction using Conditional Histogramscited by 77
  5. 2020Practical One-Shot Federated Learning for Cross-Silo Settingcited by 76
  6. 2021Differentially Private Federated Learning on Heterogeneous Datacited by 74
  7. 2019Adaptive, Distribution-Free Prediction Intervals for Deep Networkscited by 56
  8. 2022FedPop: A Bayesian Approach for Personalised Federated Learningcited by 35
  9. 2022Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Datacited by 31
  10. 2019One-Shot Federated Learning: Theoretical Limits and Algorithms to Achieve Themcited by 23
  11. 2021Stable Conformal Prediction Setscited by 19
  12. 2020Federated Stochastic Gradient Langevin Dynamicscited by 16
  13. 2021Private Prediction Setscited by 16
  14. 2021Distribution-Free Federated Learning with Conformal Predictionscited by 16
  15. 2022Federated Calibration and Evaluation of Binary Classifierscited by 5
  16. 2022Private Federated Statistics in an Interactive Settingcited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 9; 2025: 6; 2026: 8.

26 papers.

Sort:

By area:

  1. 2024An Information Theoretic Perspective on Conformal Predictioncited by 21
  2. 2024FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Modelscited by 16
  3. 2025Wasserstein-regularized Conformal Prediction under General Distribution Shiftcited by 14
  4. 2024Conformal online model aggregationcited by 11
  5. 2024Multi-Source Conformal Inference Under Distribution Shiftcited by 11
  6. 2023Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Predictioncited by 9
  7. 2023Efficient Conformal Prediction under Data Heterogeneitycited by 9
  8. 2024Certifiably Byzantine-Robust Federated Conformal Predictioncited by 8
  9. 2025One-shot Federated Learning Methods: A Practical Guidecited by 6
  10. 2024Conformal Distributed Remote Inference in Sensor Networks Under Reliability and Communication Constraintscited by 5
  11. 2025Distributed Conformal Prediction via Message Passingcited by 4
  12. 2026Efficient Federated Conformal Prediction with Group-Conditional Guaranteecited by 4
14 more
  1. 2024Marginal and training-conditional guarantees in one-shot federated conformal predictioncited by 3
  2. 2025FedCF: Fair Federated Conformal Predictioncited by 3
  3. 2024DFDG: Data-Free Dual-Generator Adversarial Distillation for One-Shot Federated Learningcited by 2
  4. 2025Federated Conditional Conformal Prediction via Generative Modelscited by 2
  5. 2026Conformalized Neural Networks for Federated Uncertainty Quantification under Dual Heterogeneitycited by 2
  6. 2026Multi-Distribution Robust Conformal Predictioncited by 1
  7. 2026Multi-Agent Conformal Prediction with Personalized Statistical Validitycited by 1
  8. 2023FedPNN: One-shot Federated Classification via Evolving Clustering Method and Probabilistic Neural Network hybridcited by 0
  9. 2024Data-light Uncertainty Set Merging with Admissibilitycited by 0
  10. 2025Differentially Private Conformal Prediction via Quantile Binary Searchcited by 0
  11. 2026Communication-Efficient Byzantine-Robust Federated Conformal Prediction via Partial Sharingcited by 0
  12. 2026Stable Localized Conformal Prediction via Transductioncited by 0
  13. 2026Partial Model Sharing Improves Byzantine Resilience in Federated Conformal Predictioncited by 0
  14. 2026Multi-source conformal prediction: leveraging heterogeneity via localizationcited by 0