CWL-FLOps: A Novel Method for Federated Learning Operations at Scale

Federated Learning (FL) has attracted much attention

in recent years because it enables users with private

data sets to train a global model collaboratively without

raw data exchange. However, due to a lack of automation,

researchers often struggled to develop, deploy, track, and

manage all the data, steps, and configuration setup for all FL

participating nodes. Federated Learning Operations (FLOps)

is recently emerging in the FL community, a new methodology

for developing FL systems efficiently and continuously. Some

research works discussed approaches for FLOps, but only a

few solutions address managing FL application scenarios from

the workflow perspective. This poster proposes CWL-FLOps,

a novel CWL-based method for FLOps, which can improve

the flexibility of FL abstraction and fully automate the FL

deployment and execution by mapping high-level descriptions

onto distributed resource nodes. Our experiments demonstrate

the feasibility of describing centralized and decentralized FL

scenarios using CWL abstracted definitions without relying on

heavily customized or external software for execution.