How to Adopt Emerging Federated Learning Technologies – Myungjin Lee, Cisco Research
How to Adopt Emerging Federated Learning Technologies – Myungjin Lee, Cisco Research
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. As more state-of-the-art FL approaches are invented in the research community and industry, supporting them in an FL framework is becoming harder. 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. This talk will cover how Flame achieves this and makes it possible to specialize the application deployment with reduced development effort. Flame is released as an open source project (https://github.com/cisco-open/flame), and its flexibility and extensibility support a variety of topologies and mechanisms, and can facilitate the development of new FL methodologies.
by The Linux Foundation
linux foundation
