# Federated Inference with Reliable Uncertainty Quantification over Wireless Channels via Conformal Prediction arXiv: https://arxiv.org/abs/2308.04237 ยท HTML: https://mlsanity.com/paper/2308.04237/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Builds on 32 of its 45 references that are in the map. - First posted on arXiv 8 Aug 2023. ## Builds on (most cited first, 30 of 32) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014) - [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016) - [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017) - [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019) - [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018) - [A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://mlsanity.com/paper/2107.07511/) (2021) - [Conformal Prediction Under Covariate Shift](https://mlsanity.com/paper/1904.06019/) (2019) - [Classification with Valid and Adaptive Coverage](https://mlsanity.com/paper/2006.02544/) (2020) - [Federated Learning via Over-the-Air Computation](https://mlsanity.com/paper/1812.11750/) (2018) - [Predictive inference with the jackknife+](https://mlsanity.com/paper/1905.02928/) (2019) - [Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)](https://mlsanity.com/paper/1812.11494/) (2018) - [Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Control](https://mlsanity.com/paper/2006.05459/) (2020) - [Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis](https://mlsanity.com/paper/2101.12704/) (2021) - [Probabilistic Conformal Prediction Using Conditional Random Samples](https://mlsanity.com/paper/2206.06584/) (2022) - [Federated Conformal Predictors for Distributed Uncertainty Quantification](https://mlsanity.com/paper/2305.17564/) (2023) - [Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification](https://mlsanity.com/paper/2102.07856/) (2021) - [Predictive Inference with Feature Conformal Prediction](https://mlsanity.com/paper/2210.00173/) (2022) - [Decentralized Federated Learning via SGD over Wireless D2D Networks](https://mlsanity.com/paper/2002.12507/) (2020) - [Conformal Prediction for Federated Uncertainty Quantification Under Label Shift](https://mlsanity.com/paper/2306.05131/) (2023) - [One-Shot Federated Conformal Prediction](https://mlsanity.com/paper/2302.06322/) (2023) - [Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehicles](https://mlsanity.com/paper/2102.03401/) (2021) - [Convergence Analysis and System Design for Federated Learning over Wireless Networks](https://mlsanity.com/paper/2105.00872/) (2021) - [Private Prediction Sets](https://mlsanity.com/paper/2102.06202/) (2021) - [Distribution-Free Federated Learning with Conformal Predictions](https://mlsanity.com/paper/2110.07661/) (2021) - [Knowing When to Stop: Delay-Adaptive Spiking Neural Network Classifiers with Reliability Guarantees](https://mlsanity.com/paper/2305.11322/) (2023) - [Calibrating AI Models for Few-Shot Demodulation via Conformal Prediction](https://mlsanity.com/paper/2210.04621/) (2022) - [On Stochastic Geometry Modeling of Cellular Uplink Transmission with Truncated Channel Inversion Power Control](https://mlsanity.com/paper/1401.6145/) (2014) - [Information-Centric Grant-Free Access for IoT Fog Networks: Edge vs Cloud Detection and Learning](https://mlsanity.com/paper/1907.05182/) (2019) - [Joint Source-Channel Coding and Bayesian Message Passing Detection for Grant-Free Radio Access in IoT](https://mlsanity.com/paper/1910.11704/) (2019) ## Built on by (most cited first, 10 of 10) - [Length Optimization in Conformal Prediction](https://mlsanity.com/paper/2406.18814/) (2024) - [Efficient Conformal Prediction under Data Heterogeneity](https://mlsanity.com/paper/2312.15799/) (2023) - [Certifiably Byzantine-Robust Federated Conformal Prediction](https://mlsanity.com/paper/2406.01960/) (2024) - [Conformal Distributed Remote Inference in Sensor Networks Under Reliability and Communication Constraints](https://mlsanity.com/paper/2409.07902/) (2024) - [What If We Had Used a Different App? Reliable Counterfactual KPI Analysis in Wireless Systems](https://mlsanity.com/paper/2410.00150/) (2024) - [Distributed Conformal Prediction via Message Passing](https://mlsanity.com/paper/2501.14544/) (2025) - [MD-AirComp+: Adaptive Quantization for Blind Massive Digital Over-the-Air Computation](https://mlsanity.com/paper/2602.18332/) (2026) - [Communication-Efficient Byzantine-Robust Federated Conformal Prediction via Partial Sharing](https://mlsanity.com/paper/2602.18396/) (2026) - [Federated Martingale Posterior Samping](https://mlsanity.com/paper/2605.18554/) (2026) - [Multi-source conformal prediction: leveraging heterogeneity via localization](https://mlsanity.com/paper/2609.14531/) (2026) ## 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.