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，用于大规模客户端间的数据安全聚合。在安全聚合中，服务器将客户端的私有输入加总，最终获得结果的同时，不学习除最终之和所隐含信息外的任何单个输入内容。Flamingo聚焦于联邦学习中常见的多轮次场景，需通过连续多次对模型权重进行求和（平均）以获得优质模型。此前如Bell等人（CCS '20）提出的协议专为单轮次设计，需重复执行以适配联邦学习场景。Flamingo消除了此前协议每轮次所需的初始化设置，并引入新型轻量级容错协议，确保客户端在中途退出求和时服务器仍能获取有效结果。此外，Flamingo创新性地实现了对Bell等人提出的"客户端邻域"机制的本地化选择方案。这些技术使得Flamingo能够减少客户端与服务器间的交互次数，相较于现有工作，将完整训练过程的端到端运行时间显著缩短。我们实现并评估了Flamingo系统，结果表明其可在（扩展）MNIST与CIFAR-100数据集上安全训练神经网络，且模型收敛精度与非私有联邦学习系统相比无损失。