Abstract Federated learning is a new machine learning framework that allows mutually distrusting clients to reap the benefits from the joint training model without explicitly disclosing their private datasets. However, the high communication cost between the cloud server and clients has become the main challenge due to the limited network bandwidth. Moreover, the model parameters it shares may be utilized to perform model inversion attacks. Aimed at these problems, a new scheme for highly efficient federated learning with strong privacy preservation in cloud computing is presented. We design a lightweight encryption protocol to provide provably privacy preservation while maintaining desirable model utility. Additionally, an efficient optimization strategy is employed to enhance the training efficiency. Under the defined threat model, we prove the proposed scheme is secure against the honest-but-curious server and extreme collusion. We evaluate the effectiveness of our scheme and compare it with existing related works on MNIST and UCI Human Activity Recognition Dataset. Results show that our scheme reduces the execution time by 20% and transmitted ciphertext size by 85% on average while achieving similar accuracy as the compared secure multiparty computation (SMC) based methods.

中文翻译：

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云计算中具有强大隐私保护的高效联邦学习

摘要 联邦学习是一种新的机器学习框架，它允许相互不信任的客户从联合训练模型中获益，而无需明确披露他们的私有数据集。然而，由于网络带宽有限，云服务器和客户端之间的高通信成本成为主要挑战。此外，它共享的模型参数可用于执行模型反转攻击。针对这些问题，提出了一种新的云计算中具有强隐私保护的高效联邦学习方案。我们设计了一种轻量级加密协议，以提供可证明的隐私保护，同时保持理想的模型效用。此外，采用有效的优化策略来提高训练效率。在定义的威胁模型下，我们证明了所提出的方案对于诚实但好奇的服务器和极端勾结是安全的。我们评估了我们方案的有效性，并将其与 MNIST 和 UCI 人类活动识别数据集上的现有相关工作进行了比较。结果表明，我们的方案平均减少了 20% 的执行时间和 85% 的传输密文大小，同时实现了与比较基于安全多方计算 (SMC) 的方法相似的准确性。