论文标题
长笛：可扩展的，可扩展的框架，用于联合学习模拟的高性能
FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations
论文作者
论文摘要
在本文中，我们介绍了“联合学习实用程序和实验工具”（长笛），这是一个高性能的开源平台，用于联合学习研究和离线模拟。长笛的目的是在大规模上快速原型制作和模拟新的联邦学习算法，包括新颖的优化，隐私和通信策略。我们描述了长笛的结构，从而实现了任意联合建模方案。我们将平台与其他最先进的平台进行比较，并描述长笛的可用特征，以在主动研究的核心领域（例如优化，隐私和可扩展性）进行实验。与其他已建立的平台的比较显示，速度最高为42倍，并节省了3倍的内存足迹。还为一系列任务以及其他功能提供了平台功能的样本，例如参与客户端数量的线性缩放以及包括FedAdam，DGA，DGA等包括各种联合优化器。
In this paper we introduce "Federated Learning Utilities and Tools for Experimentation" (FLUTE), a high-performance open-source platform for federated learning research and offline simulations. The goal of FLUTE is to enable rapid prototyping and simulation of new federated learning algorithms at scale, including novel optimization, privacy, and communications strategies. We describe the architecture of FLUTE, enabling arbitrary federated modeling schemes to be realized. We compare the platform with other state-of-the-art platforms and describe available features of FLUTE for experimentation in core areas of active research, such as optimization, privacy, and scalability. A comparison with other established platforms shows speed-ups of up to 42x and savings in memory footprint of 3x. A sample of the platform capabilities is also presented for a range of tasks, as well as other functionality, such as linear scaling for the number of participating clients, and a variety of federated optimizers, including FedAdam, DGA, etcetera.
