Horizontal Linear Regression

Introduction

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

Parameter List

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

model_info:

  • name: str Model name, should be horizontal_linear_regression.

  • config:

    • input_dim: int Size of each input sample.

    • bias: int Whether having a learnable additive bias.

input:
  • trainset:

    • type: str Train dataset type, support csv.

    • path: str If type is csv, folder path of train dataset.

    • name: bool If type is csv, file name of train dataset.

    • has_id: bool If type is csv, whether dataset has id column.

    • has_label: bool If type is csv, whether dataset has label column.

  • valset:

    • type: str Validation dataset type, support csv.

    • path: str If type is csv, folder path of validation dataset.

    • name: bool If type is csv, file name of validation dataset.

    • has_id: bool If type is csv, whether dataset has id column.

    • has_label: bool If type is csv, whether dataset has label column.

output:
  • model:

    • type: str Model output format, support “file”.

    • path: str Folder path of output model.

    • name: str File name of output model.

train_info:

  • device: str Device on which the algorithm runs, support cpu.

  • interaction_params:

    • save_frequency: int Frequency (per global_epoch) to save model, set to -1 for not saving model.

  • 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”.

      • encryption:

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

        • key_bitlength: int Key length of one time pad encryption，support 64 and 128.

        • 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 L1Loss and MAPEloss.

    • metric_config: Support multiple metrics.

      • mae: Mean absolute error.

      • mse: Mean square error.

      • mape: Mean absolute percentage error.