https://github.com/jsmjie/fedbase

An easy, modularized, DIY Federated Learning framework with many baselines for individual researchers.
https://github.com/jsmjie/fedbase

fl pytorch

Last synced: 5 months ago
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An easy, modularized, DIY Federated Learning framework with many baselines for individual researchers.

• Host: GitHub
• URL: https://github.com/jsmjie/fedbase
• Owner: jsmjie
• License: mit
• Created: 2021-11-25T09:07:17.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2023-05-24T13:30:27.000Z (almost 3 years ago)
• Last Synced: 2025-08-18T14:55:09.819Z (9 months ago)
• Topics: fl, pytorch
• Language: Python
• Homepage:
• Size: 57.7 MB
• Stars: 16
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# FedBase

An easy, modularized, DIY Federated Learning framework with many baselines for individual researchers.

## Installation

[fedbase @ pypi](https://pypi.org/project/fedbase/)

```python

pip install --upgrade fedbase

```

## Baselines

1. Centralized training

2. Local training

3. FedAvg, [Communication-Efficient Learning of Deep Networksfrom Decentralized Data](https://arxiv.org/abs/1602.05629)

4. FedAvg + Finetune

5. Fedprox, [Federated Optimization in Heterogeneous Networks](https://arxiv.org/abs/1812.06127)

5. Ditto, [Ditto: Fair and Robust Federated Learning Through Personalization](https://arxiv.org/abs/2012.04221)

6. WeCFL, [On the Convergence of Clustered Federated Learning](https://arxiv.org/abs/2202.06187)

7. IFCA, [An Efficient Framework for Clustered Federated Learning](https://arxiv.org/abs/2006.04088)

8. FeSEM, [Multi-Center Federated Learning](https://arxiv.org/abs/2005.01026)

8. To be continued...

## Three steps to achieve FedAvg!

1. Data partition

2. Nodes and server simulation

3. Train and test

## Design philosophy

1. Dataset

    1. Dataset

        1. MNIST

        2. CIFAR-10

        3. Fashion-MNIST

        4. ...

    2. Dataset partition

        1. IID

        2. Non-IID

            1. Dirichlet distribution

            2. N-class

            3. ...

        3. Fake data

        4. ...

    

2. Node

    1. Local dataset

    2. Model

    3. Objective

    4. Optimizer

    5. Local update

    6. Test

3. Server

    1. Model

    2. Aggregate

    3. Distribute

4. Server & Node

    1. Topology

    2. Client sampling

    3. Exchange message

5. Baselines

    1. Global

    2. Local

    3. FedAvg

6. Visualization

## How to develop your own FL with fedbase?