用零知识证明实现联邦学习评估的隐私保护与可验证性
ZKP-FedEval: Verifiable and Privacy-Preserving Federated Evaluation using Zero-Knowledge Proofs
- 客户端用零知识证明验证本地损失低于阈值，不暴露原始数据
- 在MNIST和HAR数据集上验证，计算开销可控、通信成本低
- 无需外部依赖，支持自包含的联邦评估流程，适合隐私敏感场景
联邦学习（FL）可在不暴露原始数据的前提下实现分布式模型训练，但评估阶段共享的性能指标可能泄露敏感信息。本文提出一种新协议，引入零知识证明（ZKP）实现隐私保护且可验证的联邦评估。客户端不直接披露原始损失值，而是生成简洁证明，声明其本地损失低于预设阈值。该方法不依赖外部API，通过自包含模块实现联邦学习模拟、ZKP电路设计及实验评估，分别在MNIST（CNN模型）和人类活动识别（HAR，MLP模型）数据集上测试。评估重点包括计算开销、通信成本与可验证性，结果表明该方案在保障隐私的同时具备实际可行性。
原文摘要 · Abstract (English)
Federated Learning (FL) enables collaborative model training on decentralized data without exposing raw data. However, the evaluation phase in FL may leak sensitive information through shared performance metrics. In this paper, we propose a novel protocol that incorporates Zero-Knowledge Proofs (ZKPs) to enable privacy-preserving and verifiable evaluation for FL. Instead of revealing raw loss values, clients generate a succinct proof asserting that their local loss is below a predefined threshold. Our approach is implemented without reliance on external APIs, using self-contained modules for federated learning simulation, ZKP circuit design, and experimental evaluation on both the MNIST and Human Activity Recognition (HAR) datasets. We focus on a threshold-based proof for a simple Convolutional Neural Network (CNN) model (for MNIST) and a multi-layer perceptron (MLP) model (for HAR), and evaluate the approach in terms of computational overhead, communication cost, and verifiability.
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