---
id: 20260305-T0-03
title: "联邦推理：隐私保护协作模型服务"
title_en: "Federal Inference: Privacy-Preserving Model Collaboration"
url: https://ai.daily.yangsir.net/daily/20260305-T0-03
issue_date: 2026-03-05
publish_date: 2026-03-04T05:00:00.000Z
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2603.02214
---

# 联邦推理：隐私保护协作模型服务

arXiv论文提出联邦推理（FI）技术，允许多个独立训练的模型在推理时协作，无需共享数据或参数。该研究解决了分布式推理中的隐私问题，通过加密协议确保数据安全。实验显示，FI在保持模型性能的同时，将数据泄露风险降低了90%，适用于医疗和金融等敏感领域。

## English Version

**Federal Inference: Privacy-Preserving Model Collaboration**

An arXiv paper introduces Federal Inference (FI) technology, allowing multiple independently trained models to collaborate during inference without sharing data or parameters. This research addresses privacy issues in distributed inference through encrypted protocols ensuring data security. Experiments show FI reduces data leakage risks by 90% while maintaining model performance, making it suitable for sensitive fields like healthcare and finance.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2603.02214)

**详情页**：https://ai.daily.yangsir.net/daily/20260305-T0-03

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