Federated Conformal Prediction

Latest papers

8 papers1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Byzantine-Robust Federated RAG via Aligned Calibration and Fixed-Membership Conformal Prediction
Sep 27, 2026Prasanjit Dubey, Aritra Guha, Xiaoming HuoRetrieval-Augmented Generation Federated Conformal Prediction
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, U.S.A. · AT&T Chief Data Office
When Calibration Fails the Vulnerable Hospital: Federated Conformal Risk Control via Risk-Curve Shrinkage
Jun 18, 2026Nafis Fuad ShahidFederated Conformal Prediction Conformal Prediction
Dhaka, Bangladesh
Multi-Agent Conformal Prediction with Personalized Statistical Validity
May 30, 2026Martin V. Vejling, Christophe A. N. Biscio, Adrien Mazoyer +2Weighted Conformal Prediction Federated Conformal Prediction
Department of Electronic Systems Aalborg University, Aalborg, Denmark · Department of Mathematical Sciences Aalborg University, Aalborg, Denmark · Institut de Mathématiques de Toulouse Université de Toulouse, Toulouse, France
Anytime-Valid Federated Conformal RAG for LLM Swarms
May 27, 2026Prasanjit Dubey, Xiaoming HuoRetrieval-Augmented Generation Federated Conformal Prediction
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, U.S.A.
Partial Model Sharing Improves Byzantine Resilience in Federated Conformal Prediction
May 12, 2026Ehsan Lari, Reza Arablouei, Stefan WernerFederated Conformal Prediction Byzantine-Robust Federated Learning
Department of Electronic Systems, Norwegian University of Science and Technology, Trondheim, Norway · 2CSIRO’s Data61, Pullenvale QLD 4069, Australia · Department of Information and Communications Engineering, Aalto University, Finland
Efficient Federated Conformal Prediction with Group-Conditional Guarantee
Mar 15, 2026Haifeng Wen, Osvaldo Simeone, Hong XingFederated Conformal Prediction Group-Conditional Conformal Prediction
1IoT Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · Institute for Intelligent Networked Systems (INSI), Northeastern University London, London, UK · Department of ECE, The Hong Kong University of Science and Technology, HK SAR
Conformalized Neural Networks for Federated Uncertainty Quantification under Dual Heterogeneity
Feb 26, 2026Quang-Huy Nguyen, Jiaqi Wang, Wei-Shinn KuFederated Conformal Prediction Uncertainty Quantification
Department of Computer Science and Software Engineering, Auburn University
Communication-Efficient Byzantine-Robust Federated Conformal Prediction via Partial Sharing
Feb 20, 2026Ehsan Lari, Reza Arablouei, Stefan WernerFederated Conformal Prediction Federated Learning Aggregation
Department of Electronic Systems, Norwegian University of Science and Technology, 7491 Trondheim, Norway · Commonwealth Scientific and Industrial Research Organisation, Pullenvale, QLD 4069, Australia · Department of Information and Communications Engineering, Aalto University, 00076, Finland

Sep 27, 2026J/K move · Enter open · S save

Byzantine-Robust Federated RAG via Aligned Calibration and Fixed-Membership Conformal Prediction

Prasanjit Dubey, Aritra Guha, Xiaoming Huo

H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, U.S.A. · AT&T Chief Data Office

Retrieval-Augmented Generation Federated Conformal Prediction Byzantine-Robust Federated Learning Conformal Prediction RAG Robustness

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Retrieval-augmented generation (RAG) lets language models answer questions more accurately by consulting relevant documents. Many valuable collections, such as medical records, cannot be pooled because of privacy rules. Federated RAG leaves each collection with its owner, or node, which scores candidate answers from its own documents; a central hub combines the scores. Some nodes, called Byzantine, may be compromised, faulty, or misled by instructions hidden in documents, and report arbitrary scores. Conformal prediction returns a set containing the correct answer with a chosen probability, using a cutoff set in a calibration step on questions with known answers. An unknown group of nodes, no larger than a declared bound, may misreport both in this step and at query time. Existing methods assume every node is honest or protect only the calibration step. We observe that the honest nodes are the same in both steps. The hub therefore has all nodes score the same calibration questions, and keeps a candidate only if some plausible group of honest nodes, using its own scores in both steps, would keep it. We prove that the resulting sets contain the correct answer with the chosen probability in finite samples, whatever the Byzantine nodes report. No method using the same information can return smaller sets without risking the loss of an answer the honest nodes support. If nodes fail at random, the guarantee weakens only by the probability that more nodes fail than declared. In simulations, on real question-answering tasks including medical exams, and with language models as nodes, some hijacked, our sets reached the target whenever no more nodes misbehaved than declared, while plain averaging could miss it. They were also clearly smaller than those of simpler methods with the same protection, most of all when the declared bound was generous, so a cautious bound costs little.