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泄露用户本地梯度，确保了联邦学习环境下用户数据的隐私性。尽管现有聚合协议具有诸多优点，但通常会给参与者带来较高的通信和计算开销，且可能无法高效处理机器学习模型的大型更新向量。本文提出了e-SeaFL——一种高效、可验证的安全聚合协议，该协议在聚合过程中仅需一轮通信。e-SeaFL允许聚合服务器向参与者生成诚实聚合的证明。我们核心思想是，在现有工作对参与用户设定的类似信任假设下，引入一组辅助节点来协助聚合服务器。在可验证性方面，e-SeaFL采用了认证同态向量承诺。实验表明，对于维度高达$100,000$的高维梯度向量，用户效率相比当前最先进方法（PPML 2022）提升了五个数量级。