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系统，该系统用于在大量客户端之间实现数据的安全聚合。在安全聚合过程中，服务器可汇总客户端的私有输入并获得聚合结果，但除最终总和所隐含的信息外，不会获知任何有关单个输入的内容。Flamingo专注于联邦学习中常见的多轮场景，即通过对模型权重进行多次连续的求和（平均）来获得优质模型。现有协议（如Bell等人提出的CCS '20方案）原本针对单轮场景设计，需通过重复执行协议才能适应联邦学习环境。Flamingo消除了先前协议所需的每轮设置流程，并引入了新型轻量级容错协议，确保即使客户端在某轮求和过程中离线，服务器仍能获得有效结果。此外，Flamingo创新性地提出了一种本地化选择客户端邻域的方法（该邻域概念最初由Bell等人引入）。这些技术使Flamingo显著减少了客户端与服务器之间的交互次数，相比现有工作，完整训练过程的端到端运行时间大幅缩短。我们实现了Flamingo系统并进行评估，结果表明其能在（扩展版）MNIST和CIFAR-100数据集上安全训练神经网络，且相比非私有联邦学习系统，模型收敛时准确率未发生损失。