The Galaxy Federated Learning Framework (GFL) is an innovative decentralized federated learning framework built on blockchain technology. Utilizing the robust infrastructure of Ethereum, GFL renders crucial operations in federated learning safe and reliable through smart contracts. In this blog post, we will guide you through the quick setup and usage of GFL, ensuring you get from zero to hero in no time.

System Requirements

Before diving in, let’s make sure your system is ready:

• Python Version: GFL only supports Python 3. Ensure your version is at least 3.4.
• Pytorch Requirement: You’ll need Pytorch version 1.4.0 and torchvision version 0.5.0. For detailed installation instructions, refer to the Pytorch installation tutorial.

Installation Steps

Now that your system is ready, let’s install GFL:

shell
pip install gfl_p

Using GFL

Once installed, you can begin using GFL with the following commands. Here’s an overview of the commands available:

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

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

actions:
  init          init gfl env
  run           startup gfl
  attach        connect to gfl node

Command Breakdown

• Initialize GFL Node: To set up a GFL node in a specific directory, use:

shell
python -m gfl_p init --home datadir

• Start GFL Node: To run the GFL node in standalone mode, execute:

shell
python -m gfl_p run --home datadir

• Connect to the Node: Use any of the following methods to connect to the started node:

shell
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

Understanding the GFL Architecture

The GFL framework is a two-part system:

• Job Generator: Think of this as a factory, where a job is created using various configuration parameters, which are then distributed across the framework for training.
• Run-Time Network: Picture a bustling city; each GFL node operates like a building working in unison, continuously processing jobs based on user inputs.

Core Architecture Layers

In addition to its foundational components, GFL is structured into three core layers:

• Manager Layer:
  • Controls the actions like start, stop, and status of the nodes.
  • Provides communication avenues between nodes.
  • Syncs the jobs across the network.
• Scheduler Layer:
  • Manages job execution and synchronization parameters.
  • Schedules jobs for efficient execution on nodes.
• FL Layer:
  • Configures the environment in which jobs run.
  • Handles training and aggregation tasks.
  • Offers interfaces for user-defined actions.

Troubleshooting and Support

If you encounter issues during setup or operation, consider the following troubleshooting tips:

• Ensure that you have all the necessary dependencies installed, specifically the correct versions of Python and Pytorch.
• Check your network connection, especially if you have difficulty connecting to GFL nodes.
• If you face problems running commands, verify that you’re executing them from a command line with the correct permissions.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.