AI RESEARCH
Decentralized Conformal Novelty Detection via Quantized Model Exchange
arXiv CS.LG
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ArXi:2605.08263v1 Announce Type: cross 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.