mlsanityData to 7 Oct 2026

Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Prediction

arXiv 2308.04237 ↗First posted 8 Aug 2023cs.IT cs.AI cs.LGcited by 9

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Abstract (arXiv)

In this paper, we consider a wireless federated inference scenario in which devices and a server share a pre-trained machine learning model. The devices communicate statistical information about their local data to the server over a common wireless channel, aiming to enhance the quality of the inference decision at the server. Recent work has introduced federated conformal prediction (CP), which leverages devices-to-server communication to improve the reliability of the server's decision. With federated CP, devices communicate to the server information about the loss accrued by the shared pre-trained model on the local data, and the server leverages this information to calibrate a decision interval, or set, so that it is guaranteed to contain the correct answer with a pre-defined target reliability level. Previous work assumed noise-free communication, whereby devices can communicate a single real number to the server. In this paper, we study for the first time federated CP in a wireless setting. We introduce a novel protocol, termed wireless federated conformal prediction (WFCP), which builds on type-based multiple access (TBMA) and on a novel quantile correction strategy. WFCP is proved to provide formal reliability guarantees in terms of coverage of the predicted set produced by the server. Using numerical results, we demonstrate the significant advantages of WFCP against digital implementations of existing federated CP schemes, especially in regimes with limited communication resources and/or large number of devices.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 32 of its 45 references that are in the map. First posted on arXiv 8 Aug 2023.

Builds on

32 of 45 references.

References · 32

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20 more
  1. 2020Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Controlcited by 62
  2. 2021Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysiscited by 51
  3. 2022Probabilistic Conformal Prediction Using Conditional Random Samplescited by 47
  4. 2023Federated Conformal Predictors for Distributed Uncertainty Quantificationcited by 46
  5. 2021Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classificationcited by 39
  6. 2022Predictive Inference with Feature Conformal Predictioncited by 33
  7. 2020Decentralized Federated Learning via SGD over Wireless D2D Networkscited by 32
  8. 2023Conformal Prediction for Federated Uncertainty Quantification Under Label Shiftcited by 29
  9. 2021Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehiclescited by 26
  10. 2023One-Shot Federated Conformal Predictioncited by 26
  11. 2021Convergence Analysis and System Design for Federated Learning over Wireless Networkscited by 25
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  14. 2023Knowing When to Stop: Delay-Adaptive Spiking Neural Network Classifiers with Reliability Guaranteescited by 8
  15. 2022Calibrating AI Models for Few-Shot Demodulation via Conformal Predictioncited by 4
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 1; 2026: 3.

9 papers.

  1. 2024Length Optimization in Conformal Predictioncited by 39
  2. 2023Efficient Conformal Prediction under Data Heterogeneitycited by 9
  3. 2024Certifiably Byzantine-Robust Federated Conformal Predictioncited by 8
  4. 2024Conformal Distributed Remote Inference in Sensor Networks Under Reliability and Communication Constraintscited by 5
  5. 2024What If We Had Used a Different App? Reliable Counterfactual KPI Analysis in Wireless Systemscited by 5
  6. 2025Distributed Conformal Prediction via Message Passingcited by 4
  7. 2026Communication-Efficient Byzantine-Robust Federated Conformal Prediction via Partial Sharingcited by 0
  8. 2026Federated Martingale Posterior Sampingcited by 0
  9. 2026Multi-source conformal prediction: leveraging heterogeneity via localizationcited by 0