论文标题
安全的沙普利价值用于跨核心联合学习（技术报告）
Secure Shapley Value for Cross-Silo Federated Learning (Technical Report)
论文作者
论文摘要
Shapley值（SV）是一个公平且原则性的度量标准，用于跨性别联盟学习（Cross-Silo FL）的贡献评估，其中组织（即客户端）与参数服务器的协作进行了协作训练预测模型。但是，FL的现有SV计算方法假设服务器可以访问RAW FL模型和公共测试数据。考虑到对FL模型的新兴隐私攻击以及测试数据可能是客户的私人资产，这可能不是实践中的有效假设。因此，我们研究了交叉硅fl的安全SV计算问题。我们首先提出了HESV，这是一种仅基于同型加密（HE）以保护隐私保护的单服务器解决方案，该解决方案具有效率的限制。为了克服这些局限性，我们提出了SECSV，这是一种具有以下新颖特征的有效的两个服务器协议。首先，SECSV利用混合隐私保护方案避免使用密文 - 测试数据和模型之间的ciphertext乘法，这在HE下非常昂贵。其次，为SECSV提出了有效的安全矩阵乘法方法。第三，SECSV从策略上识别并跳过了一些测试样本，而不会显着影响评估准确性。我们的实验表明，SECSV的速度是HESV的7.2-36.6倍，计算出的SV的准确性有限。
The Shapley value (SV) is a fair and principled metric for contribution evaluation in cross-silo federated learning (cross-silo FL), wherein organizations, i.e., clients, collaboratively train prediction models with the coordination of a parameter server. However, existing SV calculation methods for FL assume that the server can access the raw FL models and public test data. This may not be a valid assumption in practice considering the emerging privacy attacks on FL models and the fact that test data might be clients' private assets. Hence, we investigate the problem of secure SV calculation for cross-silo FL. We first propose HESV, a one-server solution based solely on homomorphic encryption (HE) for privacy protection, which has limitations in efficiency. To overcome these limitations, we propose SecSV, an efficient two-server protocol with the following novel features. First, SecSV utilizes a hybrid privacy protection scheme to avoid ciphertext--ciphertext multiplications between test data and models, which are extremely expensive under HE. Second, an efficient secure matrix multiplication method is proposed for SecSV. Third, SecSV strategically identifies and skips some test samples without significantly affecting the evaluation accuracy. Our experiments demonstrate that SecSV is 7.2-36.6 times as fast as HESV, with a limited loss in the accuracy of calculated SVs.
