Secure aggregation protocols ensure the privacy of users' data in the federated learning settings by preventing the disclosure of users' local gradients. Despite their merits, existing aggregation protocols often incur high communication and computation overheads on the participants and might not be optimized to handle the large update vectors for machine learning models efficiently. This paper presents e-SeaFL, an efficient, verifiable secure aggregation protocol taking one communication round in aggregation. e-SeaFL allows the aggregation server to generate proof of honest aggregation for the participants. Our core idea is to employ a set of assisting nodes to help the aggregation server, under similar trust assumptions existing works placed upon the participating users. For verifiability, e-SeaFL uses authenticated homomorphic vector commitments. Our experiments show that the user enjoys five orders of magnitude higher efficiency than the state of the art (PPML 2022) for a gradient vector of a high dimension up to $100,000$.

翻译：安全聚合协议通过防止用户本地梯度泄露，在联邦学习场景中保障用户数据隐私。然而，现有聚合协议虽具优势，但常导致参与者面临高昂的通信与计算开销，且可能无法高效处理机器学习模型的大规模更新向量。本文提出e-SeaFL——一种高效且可验证的安全聚合协议，其聚合过程仅需一轮通信。e-SeaFL允许聚合服务器为参与者生成诚实聚合证明。我们的核心思路是在与现有工作对参与用户相似的信任假设下，引入辅助节点集群协助聚合服务器。在可验证性方面，e-SeaFL采用认证同态向量承诺机制。实验表明，对于维度高达$100,000$的梯度向量，用户端效率相较于现有最优方案（PPML 2022）提升五个数量级。