Decentralized Conformal Novelty Detection via Quantized Model Exchange

• URL: http://arxiv.org/abs/2605.08263v1
• Date: Thu, 07 May 2026 23:46:42 GMT
• Title: Decentralized Conformal Novelty Detection via Quantized Model Exchange
• Abstract summary: We study novelty detection with global false discovery rate (FDR) control across heterogeneous composite null distributions.<n>We propose a framework based on the exchange of quantized surrogate models, allowing independent agents to share low-precision representations.
• Score: 2.8241645653117042
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This work studies decentralized novelty detection with global false discovery rate (FDR) control across heterogeneous composite null distributions, without sharing the raw data due to privacy and bandwidth considerations. We propose a framework based on the exchange of quantized surrogate models, allowing independent agents to share low-precision representations of locally learned non-conformity score functions. We prove that evaluating data against these quantized composite scores preserves conditional exchangeability, providing rigorous finite-sample guarantees for global FDR control. Empirical studies on synthetic datasets confirm our theoretical results, demonstrating that the proposed approach maintains competitive statistical power while drastically reducing the communication cost.

Related papers

• A Posterior-Predictive Variance Decomposition for Epistemic and Aleatoric Uncertainty in Wind Power Forecasting [3.68407754316989]
  This paper applies the law of total variance to the joint setting of neural network regression and Bayesian posterior approximation.<n>A wind power-specific evaluation framework is proposed to validate disentanglement without access to ground-truth uncertainty labels.<n>Across synthetic and real-world experiments, the decomposed AU and EU components respond in theoretically consistent directions.
  arXiv  Detail & Related papers  (2026-05-21T12:23:14Z)
• Benchmarking Few-shot Transferability of Pre-trained Models with Improved Evaluation Protocols [123.73663884421272]
  Few-shot transfer has been revolutionized by stronger pre-trained models and improved adaptation algorithms.<n>We establish FEWTRANS, a comprehensive benchmark containing 10 diverse datasets.<n>By releasing FEWTRANS, we aim to provide a rigorous "ruler" to streamline reproducible advances in few-shot transfer learning research.
  arXiv  Detail & Related papers  (2026-02-28T05:41:57Z)
• Event-Triggered Gossip for Distributed Learning [61.70659996356528]
  We develop a new event-triggered gossip framework for distributed learning to reduce inter-node communication.<n>We analyze bf71.61% with only a marginal performance loss, compared with the conventional full-text-of-the-art distributed learning methods.
  arXiv  Detail & Related papers  (2026-02-22T10:13:43Z)
• Evidential Trust-Aware Model Personalization in Decentralized Federated Learning for Wearable IoT [16.700861880781087]
  Decentralized federated learning (DFL) enables collaborative model training across edge devices without centralized coordination.<n>We present Murmura, a framework that leverages evidential deep learning to enable trust-aware model personalization in DFL.
  arXiv  Detail & Related papers  (2025-12-22T08:26:54Z)
• MMDCP: A Distribution-free Approach to Outlier Detection and Classification with Coverage Guarantees and SCW-FDR Control [6.429952624399788]
  We propose a unified framework for multi-class classification and outlier detection under label shift.<n>The Modified Mahalanobis Distance Conformal Prediction (MMDCP) combines class-specific distance measures with full conformal prediction to construct a score function.<n>We provide the first theoretical characterization of the gap between oracle and empirical conformal $p$-values, which ensures valid coverage and effective control of the class-wise false discovery rate (CW-FDR)
  arXiv  Detail & Related papers  (2025-11-15T03:48:44Z)
• Heterogeneous Multisource Transfer Learning via Model Averaging for Positive-Unlabeled Data [2.030810815519794]
  We propose a novel transfer learning framework that integrates information from heterogeneous data sources without direct data sharing.<n>For each source domain type, a tailored logistic regression model is conducted, and knowledge is transferred to the PU target domain through model averaging.<n>Our method outperforms other comparative methods in terms of predictive accuracy and robustness, especially under limited labeled data and heterogeneous environments.
  arXiv  Detail & Related papers  (2025-11-14T03:15:31Z)
• Noise-Adaptive Conformal Classification with Marginal Coverage [53.74125453366155]
  We introduce an adaptive conformal inference method capable of efficiently handling deviations from exchangeability caused by random label noise.<n>We validate our method through extensive numerical experiments demonstrating its effectiveness on synthetic and real data sets.
  arXiv  Detail & Related papers  (2025-01-29T23:55:23Z)
• Strength of statistical evidence for genuine tripartite nonlocality [0.0]
  Recent advancements in network nonlocality have led to the concept of local operations and shared randomness-based genuine multipartite nonlocality (LOSR-GMNL) This paper focuses on a tripartite scenario where the goal is to exhibit correlations impossible in a network where each two-party subset shares bipartite resources and every party has access to unlimited shared randomness.
  arXiv  Detail & Related papers  (2024-07-28T21:12:52Z)
• VALID: a Validated Algorithm for Learning in Decentralized Networks with Possible Adversarial Presence [13.612214163974459]
  We introduce the paradigm of validated decentralized learning for undirected networks with heterogeneous data. VALID protocol is the first to achieve a validated learning guarantee. Remarkably, VALID retains optimal performance metrics in adversary-free environments.
  arXiv  Detail & Related papers  (2024-05-12T15:55:43Z)
• Breaking the Spurious Causality of Conditional Generation via Fairness Intervention with Corrective Sampling [77.15766509677348]
  Conditional generative models often inherit spurious correlations from the training dataset. This can result in label-conditional distributions that are imbalanced with respect to another latent attribute. We propose a general two-step strategy to mitigate this issue.
  arXiv  Detail & Related papers  (2022-12-05T08:09:33Z)
• Error-based Knockoffs Inference for Controlled Feature Selection [49.99321384855201]
  We propose an error-based knockoff inference method by integrating the knockoff features, the error-based feature importance statistics, and the stepdown procedure together. The proposed inference procedure does not require specifying a regression model and can handle feature selection with theoretical guarantees.
  arXiv  Detail & Related papers  (2022-03-09T01:55:59Z)
• Non-Linear Spectral Dimensionality Reduction Under Uncertainty [107.01839211235583]
  We propose a new dimensionality reduction framework, called NGEU, which leverages uncertainty information and directly extends several traditional approaches. We show that the proposed NGEU formulation exhibits a global closed-form solution, and we analyze, based on the Rademacher complexity, how the underlying uncertainties theoretically affect the generalization ability of the framework.
  arXiv  Detail & Related papers  (2022-02-09T19:01:33Z)
• Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [100.52588638477862]
  We develop an approximate Bayesian inference scheme based on posterior regularisation. We demonstrate the utility of our method in the context of transferring prognostic models of prostate cancer across globally diverse populations.
  arXiv  Detail & Related papers  (2020-06-26T13:50:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.