通过量化模型交换实现去中心化异常检测，兼顾隐私与通信效率。
Decentralized Conformal Novelty Detection via Quantized Model Exchange
- 各节点交换低精度的替代模型，避免原始数据共享。
- 理论证明可保持全局误报率控制，且在合成数据上表现稳定。
- 适合对隐私和通信成本敏感的分布式系统应用。
本文研究在异构复合零假设分布下，通过不共享原始数据（出于隐私和带宽考虑），实现去中心化的异常检测并控制全局假发现率（FDR）。我们提出一种基于量化代理模型交换的框架，使独立智能体能够共享本地学习的非符合性评分函数的低精度表示。我们证明，基于这些量化复合评分评估数据能保持条件可交换性，从而为全局FDR控制提供严格的有限样本保证。在合成数据集上的实证研究验证了理论结果，表明该方法在显著降低通信成本的同时，仍保持具有竞争力的统计功效。
原文摘要 · Abstract (English)
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.
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