Horizontal DenseNet

Introduction

Horizontal DenseNet model is a model obtained by building the model DenseNet proposed in the paper “Densely Connected Convolutional Networks” on the horizontal federation system, and is implemented based on the deep learning framework.

Parameter List

identity: str Federated identity of the party, should be one of “label_trainer” or “assist trainer”.

model_info:

• name: str Model name, should be “horizontal_densenet”.

• config:

  • num_classes: int Number of output classes.

  • layers: int DenseNet layers, support 121, 169, 201 and 264.

input:

• trainset:

  • type: str Train dataset file type, such as “npz”.

  • path: str Folder path of train dataset.

  • name: str File name of train dataset.

• valset:

  • type: str Validation dataset file type, such as “npz”.

  • path: str Folder path of Validation dataset.

  • name: str File name of Validation dataset.

output:

• model:

  • type: str Model output file format.

  • path: str Folder path of output model.

  • name: str File name of output model.

• metrics:

  • type: str Metrics output file format.

  • path: str Folder path of output metrics.

  • header: bool Whether to include the column name.

• evaluation:

  • type: str Evaluation output file format.

  • path: str Folder path of output Evaluation.

  • header: bool Whether to include the column name.

train_info:

• device: str Device on which the algorithm runs, support “cpu” and specified gpu device such as cuda:0.

• interaction_params

  • save_frequency: int Number of epoches of model saving interval.

  • save_probabilities: bool Whether to save the probability of model output.

  • save_probabilities_bins_number: int Number of bins of probability histogram.

  • write_training_prediction: bool Whether to save the prediction of training set.

  • write_validation_prediction: bool Whether to save the prediction of validation set.

  • echo_training_metrics: bool Whether to print the metrics of training set.

• params:

  • global_epoch: int Global training epoch.

  • local_epoch: int Local training epoch of involved parties.

  • batch_size: int Batch size of samples in local and global process.

  • aggregation_config:

    • type: str Aggregation method, support “fedavg”, “fedprox” and “scaffold”.

    • encryption:

      • method: str Encryption method, recommend “otp”.

      • key_bitlength: int Key length of one time pad encryption, support 64 and 128. 128 is recommended for better security.

      • data_type: str Input data type, support “torch.Tensor” and “numpy.ndarray”, depending on model data type.

      • key_exchange:

        • key_bitlength: int Bit length of paillier key, recommend to be greater than or equal to 2048.

        • optimized: bool Whether to use optimized method.

      • csprng:

        • name: str Pseudo-random number generation method.

        • method: str Corresponding hash method.

  • optimizer_config: Support optimizers and their parameters defined in PyTorch or registered by user. For example:

    • SGD:

      • lr: float Learning rate.

      • momentum: float Momentum.

      • weight_decay: float Weight decay rate.

  • lr_scheduler_config: Support lr_scheduler and their parameters defined in PyTorch or registered by user. For example:

    • CosinAnnealingLR:

      • T_max: int Maximum iterations.

  • lossfunc_config: Support lossfunc and their parameters defined in PyTorch or registered by user. For example:

    • CrossEntropyLoss:

  • metric_config: Support multiple metrics.

    • accuracy: Accuracy.

    • precision: Precision.

    • recall: Recall.

    • f1_score: F1 score.

    • auc: Area Under Curve.

    • ks: Kolmogorov-Smirnov (KS) Statistics.

  • early_stopping:

    • key: str Indicators of early stop strategy, such as “acc”.

    • patience: int Tolerance number of early stop strategy.

    • delta: float Tolerance range of early stop strategy.