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FedELMY: One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity

This repository is the official implementation of the ACM Multimedia 2024 paper "FedELMY: One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity".

Supplementary Material

The supplementary material for the paper can be found here.

Code

Requirements

  • This codebase is written for python3 (used Python 3.7.4 while implementing).

With Anaconda, you can create an environment FedELMY with the following command:

conda create -n FedELMY python=3.7.4

then, activate the environment with:

conda activate FedELMY
  • We use Pytorch version of 1.13.1, 11.6 CUDA version.
  • To install necessary Python packages,
pip install -r requirements.txt

How to Run Codes?

The sample code is as follows:

python3 FedELMY.py --model resnet18 --dataset cifar10 --num_users 10 --betas 0.5 --num_models 5 --alpha 1 --beta 1 --seed 1  --device cuda:0

Here is an explanation of some parameters,

  • --model: model architecture to be used (e.g., cnn or resnet18).
  • --dataset: name of the datasets (e.g., cifar10, pacs, oc10 or tiny).
  • --num_users: the number of total clients (e.g., 10, 20, 50).
  • --betas: concentration parameter beta for latent Dirichlet Allocation when splitting data (default: 0.5).
  • --num_models: the number of models in the model pool (e.g., 5, 10, 20).
  • --alpha: the scale control hyperparameter of the distance between the current model to all models within the model pool. (default: 1).
  • --beta: the scale control hyperparameter of the distance between the current model to the first model of the model pool. (default: 1).
  • --seed: the random seed to be used (e.g., 1, 2, 3).
  • --device: the device to be used for training (e.g., cuda:0 or cpu).

Note that the same random seed must be fixed for fair comparison. Because different random seeds mean that the data distribution on each client is different.

Citing this work

If you find this code useful in your research, please consider citing:

@inproceedings{wangfedelmy,
  title={FedELMY: One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity},
  author={Naibo Wang, Yuchen Deng, Wenjie Feng, Shichen Fan, Jianwei Yin, and See-Kiong Ng},
  booktitle={ACM Multimedia},
  year={2024},
  doi={10.1145/3664647.3681054}
}

Project Structure

  • /checkpoints

    Used to store model checkpoints during training, allowing for recovery and resuming training.

  • /dataset_info

    Contains metadata and information regarding datasets used by the models.

  • /helpers

    • __init__.py: Indicates that this directory is a Python package and can contain common initializations for the helper's module.
    • comm_helpers.py: Provides helper functions for common tasks across the project.
    • datasets.py: Contains functions related to loading and processing datasets.
    • office_caltech_10.py: Specific functions for handling the Office-Caltech 10 dataset.
    • optimizers.py: Custom or modified optimization algorithms used in training models.
    • pacs.py: Functions tailored for the PACS dataset, often used in domain adaptation.
    • sam.py: Implements the Sharpness-Aware Minimization (SAM) optimization technique.
    • utils.py: General utility functions that are used across different modules.

  • /models

    • __init__.py: Initializes the model's directory as a Python package.
    • nets.py: Contains definitions for different neural network architectures.
    • resnet.py: Contains the implementation of Residual Network architectures.
    • vit.py: Implementation of Vision Transformer models.
    • wide_resnet.py: Wide Residual Network implementations, a variant of ResNet with more width.
    • wrn.py: Another variant for Wide Residual Networks.

  • /results

    Directory for storing the output results from model evaluations, such as accuracy metrics and loss values.

  • /

    • hps.py: Defines hyperparameters for hyperparameter selection.
    • FedELMY.py: The main script with the core logic for the FedELMY framework's functionality.
    • loop_df_fl.py: Basic functions for running the FedELMY framework.
    • README.md: Provides documentation and an overview of the project.
    • requirements.txt: Specifies all the necessary Python packages for the project.
    • warmup_config.py: Configuration settings for the warmup phase at the beginning of training.