Distributed machine learning approaches, including a broad class of federated learning (FL) techniques, present a number of benefits when deploying machine learning applications over widely distributed infrastructures. The benefits are highly dependent on the details of the underlying machine learning topology, which specifies the functionality executed by the participating nodes, their dependencies and interconnections. Current systems lack the flexibility and extensibility necessary to customize the topology of a machine learning deployment. We present Flame, a new system that provides flexibility of the topology configuration of distributed FL applications around the specifics of a particular deployment context, and is easily extensible to support new FL architectures. Flame achieves this via a new high-level abstraction Topology Abstraction Graphs (TAGs). TAGs decouple the ML application logic from the underlying deployment details, making it possible to specialize the application deployment with reduced development effort. Flame is released as an open source project, and its flexibility and extensibility support a variety of topologies and mechanisms, and can facilitate the development of new FL methodologies.

翻译：分布式机器学习方法，包括一大类联邦学习技术，在广泛分布的基础设施上部署机器学习应用时具有诸多优势。这些优势高度依赖于底层机器学习拓扑的细节，该拓扑规定了参与节点执行的功能、节点间的依赖关系及互连方式。当前系统缺乏定制机器学习部署拓扑所需的灵活性和可扩展性。我们提出Flame，这一新系统能够根据特定部署环境灵活配置分布式联邦学习应用的拓扑，并可轻松扩展以支持新的联邦学习架构。Flame通过一种新型高层抽象——拓扑抽象图（TAGs）实现这一目标。TAGs将机器学习应用逻辑与底层部署细节解耦，从而以更低的开发成本实现应用部署的专门化。Flame已作为开源项目发布，其灵活性和可扩展性支持多种拓扑结构和机制，并能促进新型联邦学习方法论的开发。