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Openreview Pub github Lisence

Robust Training of Federated Models with
Extremely Label Deficiency (ICLR 2024)

Yonggang Zhang*, Zhiqin Yang*, Xinmei Tian, Nannan Wang, Tongliang Liu, Bo Han

Keywords: Fderated Learning, Label Deficiency, Gradient Conflict.

Abstract:Federated semi-supervised learning (FSSL) has emerged as a powerful paradigm for collaboratively training machine learning models using distributed data with label deficiency. Advanced FSSL methods predominantly focus on training a single model on each client. However, this approach could lead to a discrepancy between the objective functions of labeled and unlabeled data, resulting in gradient conflicts. To alleviate gradient conflict, we propose a novel twin-model paradigm, called Twin-sight, designed to enhance mutual guidance by providing insights from different perspectives of labeled and unlabeled data. In particular, Twin-sight concurrently trains a supervised model with a supervised objective function while training an unsupervised model using an unsupervised objective function. To enhance the synergy between these two models, Twin-sight introduces a neighbourhood-preserving constraint, which encourages the preservation of the neighbourhood relationship among data features extracted by both models. Our comprehensive experiments on four benchmark datasets provide substantial evidence that Twin-sight can significantly outperform state-of-the-art methods across various experimental settings, demonstrating the efficacy of the proposed Twin-sight.

The Table of Contents

😉 If Twin-sight is helpful to you, please star this repo. Thanks! 🤗

😬 Dependencies and installation

Before running or modifying the code, you need to:

  • Make sure Anaconda or Miniconda is installed.

  • Clone this repo to your machine.

    # git clone this repository
git clone https://github.com/visitworld123/Twin-sight
cd Twin-sight

# create new anaconda env 
conda create -n Twin-sight python=3.8 -y
conda activate Twin-sight

  • required environment in environment.yaml

    # install environment dependencies
conda env create -f environment.yaml

  • required packages in requirements.txt

    # install python dependencies
pip install -r requirements.txt

🥳 How to run:

python main.py

In config.yaml, you can change the hyper-parameters and some settings. In our experiments, we use wandb as the default tool to record the running metrics like Acc and Loss. Change recording tool: If you want to use tensorboard to record, you can set record_tool = 'tensorboard.

Please refer to the ./configs/default.py file for the list of hyper-parameters. We list some main parameters and its meaning for quick start.

You can set these parameters by modify default.py or a new yaml file and use it by python main.py --config_file xxx.yaml to indicate a certain config file.

--client_num_in_total: The total number of clients in FL system, you can set 10 and 100 to reproduce our experiments.

--client_num_per_round: The number of sampled clients every round, also called sampling rate.

--partition_alpha: Our main results based on LDA partition method, you can set 0.1 and 0.05.

--global_epochs_per_round: Local epochs for every client.

--comm_round: Global Communication round.

--SSFL_setting: Which SSFL setting we standalone, you can choose partial_client or partial_data. Partial_client (PC) means partial clients have fully-labeled data, and other clients have fully-unlabeled data. It is also the focus of our discussion in this paper. Partial_data (PD) means every client has partially labeled data.

--SSL_method: The pesudo-label strategy. We can choose fixmatch or freematch.

--model: There are three different models we can choose which represents different self-supervised model including SemiFed_SimCLR, SemiFed_SimSiam, SemiFed_BYOL.

🌲 Detail of our method:

The main code of Twin-sight is in trainers/normal_trainer.py. In this file, the function of train_semiFed_model_labeled_client_PC performs the training process of the fully-labeled client under a partial_client setting. At the same time, train_semiFed_model_unlabeled_client_PC conducts training of fully-unlabeled clients under this setting.

model/SemiFed.py includes the model architecture of each local client. It is consisted of a unsup_model (SimCLR, BYOL, SimSiam) and a sup_model (ResNet18 in our method).

🌹 Experimental results

😃 Citation

If our work is useful for your research, please consider citing:

@inproceedings{
    zhang2024robust,
    title={Robust Training of Federated Models with Extremely Label Deficiency},
    author={Yonggang Zhang and Zhiqin Yang and Xinmei Tian and Nannan Wang and Tongliang Liu and Bo Han},
    booktitle={The Twelfth International Conference on Learning Representations},
    year={2024},
    url={https://openreview.net/forum?id=qxLVaYbsSI}
}

@inproceedings{
    yang2023fedfed,
    title={FedFed: Feature Distillation against Data Heterogeneity in Federated Learning},
    author={Zhiqin Yang and Yonggang Zhang and Yu Zheng and Xinmei Tian and Hao Peng and Tongliang Liu and Bo Han},
    booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
    year={2023}
}

📕 License

This project is licensed under MIT. Redistribution and use should follow this license.

🥰 Acknowledgement

This project is partly based on VHL and FedEMA

☎️ Contact

If you have any questions, please feel free to reach me out at yangzqccc@buaa.edu.cn.

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