GFL Framework

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Galaxy Federated Learning Framework(GFL) is a decentralized federated learning framework based on blockchain. GFL builds a decentralized communication network based on Ethereum, and executes key operations that requires credibility in FL through smart contracts.

Quick Start

1. System Envs Required

a) GFL only supports Python3， please make sure your python version is no less than 3.4.

b) GFL is based on Pytorch, so, torch>=1.4.0 and torchvision>=0.5.0 is required before using GFL. Pytorch installation tutorial

2. Install

pip install gfl_p

3. Usage

The available commands of GFL.

usage: GFL [-h] {init,app,attach} ...

optional arguments:
  -h, --help         show this help message and exit

actions:
  {init,app,attach}
    init             init gfl env
    run              startup gfl
    attach           connect to gfl node

Init GFL node in datadir directory.

python -m gfl_p init --home datadir

Start GFL node(start in standalone mode by default). If you need to open console when starting node, use the `--console`` argument.

python -m gfl_p run --home datadir

Open console for operating GFL node. The following three methods can be used to connect to the node started in the previous step.

python -m gfl attach						# connect to http://localhost:9434 in default
python -m gfl attach -H 127.0.0.1 -P 9434
python -m gfl attach --home datadir

GFL base design

The GFL framework is divided into two parts:

Job Generator

Used to create a job that can be executed in the GFL network. Developers can use the interface provided by GFL to generate a Job for various configuration parameters and distribute them to the network for training.

Run-Time Network

Several running nodes build GFL's decentralized training network, and each GFL node is also a node in the blockchain. These nodes continuously process the jobs to be trained in the network according to user commands.

GFL core arch

• Manager Layer

  • The start/stop/status operation of node
  • Provide communication interface for nodes
  • Sync job

• Scheduler Layer

  • Manage the execution process of each job
  • Synchronize parameter files among nodes
  • Schedule the execution order of multiple jobs on the node

• FL Layer

  • Configure the running environment of the job
  • Perform training/aggregation tasks
  • Provide the interfaces of user-defined action