Horizontal Logistic Regression

Introduction

Horizontal logistic regression is a model obtained by building logistic regression model on the horizontal federated learning system.

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_logistic_regression”.

  • config:

    • input_dim: int Number of features of input data, can be Null.

    • bias: bool Whether having a learnable additive bias.

input:
  • trainset:

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

    • path: str Folder path of train dataset.

    • name: str File name of train dataset.

    • has_label: bool Whether dataset has label column.

    • has_id: bool Whether dataset has id column.

  • valset:

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

    • path: str Folder path of Validation dataset.

    • name: str File name of Validation dataset.

    • has_label: bool Whether dataset has label column.

    • has_id: bool Whether dataset has id column.

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”.

  • 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:
      • Adam:

        • lr: float Optimizer learning rate.

        • amsgrad: bool Whether to use the AMSGrad variant.

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

        • step_size: int Period of learning rate decay.

        • gamma: float Multiplicative factor of learning rate decay.

    • lossfunc_config: Loss function configuration, support “BCEWithLogitsLoss”.

    • metric_config: Support multiple metrics.

      • accuracy: Accuracy.

      • precision: Precision.

      • recall: Recall.

      • f1_score: F1 score.

      • auc: Area Under Curve.

      • ks: Kolmogorov-Smirnov (KS) Statistics.