A powerful framework for decentralized federated learning with user-defined communication topology

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Networking scatterbrained
Overview

Scatterbrained

Decentralized Federated Learning

PyPI - Python Version GitHub last commit GitHub

Scatterbrained makes it easy to build federated learning systems. In addition to traditional federated learning, Scatterbrained supports decentralized federated learning — a new, cooperative type of federated learning where the learning is done by a group of peers instead of by a centralized server. For more information, see our 2021 paper, Scatterbrained: A flexible and expandable pattern for decentralized machine learning.

You can use your favorite machine learning frameworks alongside Scatterbrained, such as TensorFlow, SciKit-Learn, or PyTorch.

Usage

For examples of how to get started using Scatterbrained, see the Examples directory.

Installation

You can install Scatterbrained with pip:

pip install scatterbrained

If you would rather download and install from source, you can do so with the following:

git clone https://github.com/JHUAPL/scatterbrained.git
cd scatterbrained

You must first install the dependencies with:

pip3 install -r ./requirements/requirements.txt

And then you can install the package with:

pip3 install -e .

License

The code in this repository is released under an Apache 2.0 license. For more information, see LICENSE.

Copyright 2021 The Johns Hopkins Applied Physics Laboratory

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

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Comments
  • Sufficient Explanation needed for clear understanding and using

    Sufficient Explanation needed for clear understanding and using

    hi @j6k4m8 It is throwing an error when Iam following steps given in Readme file. it is not clear for me. How to use this package and check model updates at different clients. please provide step by step explanation with an example or any tutorial.

    MicrosoftTeams-image

    Thanks and Regards Prathap

    opened by prathapkumarbaratam 0
Owner
Johns Hopkins Applied Physics Laboratory
Johns Hopkins Applied Physics Laboratory
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