Federated learning has been widely applied as a distributed machine learning method in various fields, allowing a global model to be trained by sharing local gradients instead of raw data. However, direct sharing of local gradients still carries the risk of privacy data leakage, and the malicious server might falsify aggregated result to disrupt model updates. To address these issues, a lot of privacy-preserving and verifiable federated learning schemes have been proposed. However, existing schemes suffer from significant computation overhead in either encryption or verification. In this paper, we present ESVFL, an efficient and secure verifiable federated learning scheme with privacy-preserving. This scheme can simultaneously achieve low computation overhead for encryption and verification on the user side. We design an efficient privacy-preserving method to encrypt the users’ local gradients. Using this method, the computation and communication overheads of encryption on the user side is independent of the number of users. Users can efficiently verify the correctness of aggregated results returned by the cloud servers using cross-verification. During the verification process, there is no interaction among users and no additional computation is required. Furthermore, we also construct an efficient method to address the issue of user dropout. When some users drop out, online users do not incur any additional computation and communication overheads, while guaranteeing the correctness of the aggregated result of online users’ encrypted gradients. The security analysis and the performance evaluation demonstrate that ESVFL is secure and can achieve efficient encryption and verification.

中文翻译：

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ESVFL：高效、安全、可验证、隐私保护的联邦学习


联邦学习作为一种分布式机器学习方法已广泛应用于各个领域，允许通过共享局部梯度而不是原始数据来训练全局模型。然而，直接共享本地梯度仍然存在隐私数据泄露的风险，并且恶意服务器可能会伪造聚合结果以破坏模型更新。为了解决这些问题，人们提出了许多保护隐私和可验证的联邦学习方案。然而，现有方案在加密或验证方面都面临着巨大的计算开销。在本文中，我们提出了 ESVFL，一种高效、安全、可验证且具有隐私保护的联邦学习方案。该方案可以同时实现用户侧加密和验证的低计算开销。我们设计了一种有效的隐私保护方法来加密用户的本地梯度。采用这种方法，用户侧加密的计算和通信开销与用户数量无关。用户可以通过交叉验证有效地验证云服务器返回的聚合结果的正确性。验证过程中，用户之间没有交互，也不需要额外的计算。此外，我们还构建了一种有效的方法来解决用户流失问题。当部分用户退出时，在线用户不会产生任何额外的计算和通信开销，同时保证在线用户加密梯度聚合结果的正确性。安全分析和性能评估表明ESVFL是安全的，可以实现高效的加密和验证。