This paper introduces Flamingo, a system for secure aggregation of data across a large set of clients. In secure aggregation, a server sums up the private inputs of clients and obtains the result without learning anything about the individual inputs beyond what is implied by the final sum. Flamingo focuses on the multi-round setting found in federated learning in which many consecutive summations (averages) of model weights are performed to derive a good model. Previous protocols, such as Bell et al. (CCS '20), have been designed for a single round and are adapted to the federated learning setting by repeating the protocol multiple times. Flamingo eliminates the need for the per-round setup of previous protocols, and has a new lightweight dropout resilience protocol to ensure that if clients leave in the middle of a sum the server can still obtain a meaningful result. Furthermore, Flamingo introduces a new way to locally choose the so-called client neighborhood introduced by Bell et al. These techniques help Flamingo reduce the number of interactions between clients and the server, resulting in a significant reduction in the end-to-end runtime for a full training session over prior work. We implement and evaluate Flamingo and show that it can securely train a neural network on the (Extended) MNIST and CIFAR-100 datasets, and the model converges without a loss in accuracy, compared to a non-private federated learning system.

翻译：本文介绍了Flamingo——一种面向大规模客户端数据安全聚合的系统。在安全聚合中，服务器在仅知最终求和结果的前提下，对客户端的私有输入进行求和计算，而不会泄露任何单个输入信息。Flamingo主要针对联邦学习中的多轮场景，该场景需要通过连续多次对模型权重进行求和（平均）以获得优质模型。现有协议（如Bell等人在CCS '20提出的方案）专为单轮设计，需通过重复执行协议来适配联邦学习场景。Flamingo消除了先前协议每轮设置的额外开销，并创新性地引入轻量级丢失弹性协议，确保即便客户端在求和过程中中途退出，服务器仍能获得有效结果。此外，Flamingo提出一种全新方法，用于本地选择Bell等人提出的所谓"客户端邻域"。这些技术使得Flamingo能够减少客户端与服务器之间的交互次数，从而在完整训练会话的端到端运行时中较先前工作实现显著缩短。我们实现了Flamingo系统并开展评估，实验表明：在（扩展版）MNIST和CIFAR-100数据集上，该系统可安全训练神经网络，且与非私有联邦学习系统相比，模型收敛时精度无损失。