FLorist

FLorist is a platform to launch and monitor Federated Learning (FL) training jobs. Its goal is to bridge the gap between state-of-the-art FL algorithm implementations and their applications by providing a system to easily kick off, orchestrate, manage, collect, and summarize the results of FL4Health training jobs.

As Federated Learning has a client and a server side, FLorist also has client and server-side “modes” to orchestrate the training process on both sides. When FLorist’s client long-running process is started, they will be waiting for instructions from FLorist’s server to start FL clients for training. Once FLorist’s server starts the FL server, it sends instructions to FLorist’s clients to start their own FL clients. Then, FLorist’s server monitors the FL server and clients processes while collecting their progress to be displayed in the web UI.

At the end of training, it saves the results in a database and also provide access to the training artifacts (e.g. model files). For a visual representation of the system, please check the diagram below.