Configuration

In this page, we provide an overview of all the main configuration groups and values that can be used in ARMLET when running FL experiments (with the federation mode of armlet).

See Configuration prerequisites for any information about the general configuration groups and values of ARMLET.

Main configuration categories of the federation mode

The main configuration groups related to the federation mode are:

• data: for everything related to data;

• eval: for the model evaluation;

• exp: generic settings for the FL experiment;

• logger: logger configuration;

• method: for the FL algorithm and its hyper-parameters;

• protocol: for the FL protocol;

• save: for saving models.

See also

All the essential config values are explained in the subpages, but the different options (i.e., config files) for each config group are not detailed. Please look at the ARMLET_DIR/configs folder to explore the different config group possibilities.

Example

In the following, we provide an example of a YAML configuration file that can be directly used to run an FL experiment with the federation mode. Note that these configuration values are detailed in the next documentation pages.

# @package _global_

armlet:
  mode: federation

paths:
  root_dir: .
  data_dir: ./datasets
  log_dir: ./logs
  output_dir: ${hydra:runtime.output_dir}


data:

  dataset:
    dataset_name: Adult
    _target_: armlet.data.datasets.load_Adult_dataset
    path: ./datasets/Adult/raw_data
    sensitive_attributes: [age, gender, race]

  splitter:
    distribution:
      _target_: armlet.data.splitter.ArmletDataSplitter.iid
    _target_: armlet.data.splitter.ArmletDataSplitter
    client_split: 0.2
    client_val_split: 0.5
    keep_test: false
    server_test: false
    server_test_union: true
    server_split: 0.0
    server_val_split: 0.0
    uniform_test: false

  cleaning:
    missing_values:
      _target_: armlet.data.cleaning.missing_values.RemoveMV
    name: default

  processing:
    one_hot_encoding:
      _target_: armlet.data.processing.feature_encoding.one_hot_encoding_pipeline
      _apply_directly_to_subdata_: false
    conversion_to_num:
      _target_: armlet.data.processing.format_conversion.convert_bool_and_cat_to_num
      _apply_directly_to_subdata_: true
    normalization:
      _target_: armlet.data.processing.normalization.normalization_pipeline
      _apply_directly_to_subdata_: false
      cols_to_exclude: ${data.dataset.sensitive_attributes}
    conversion_to_tensors:
      _target_: armlet.data.processing.format_conversion.convert_dataframes_to_tensors
      _apply_directly_to_subdata_: true
      sensitive_attributes: ${data.dataset.sensitive_attributes}

  others:
    sampling_perc: 1.0

  seed: 42

exp:
  device: cpu
  seed: 42
  inmemory: true
  train: true

protocol:
  eligible_perc: 1.0
  n_clients: 10
  n_rounds: 150

method:

  _target_: armlet.FL_pipeline.FL_algorithms.ArmletCentralizedFL

  hyperparameters:

    client:
      batch_size: 128
      local_epochs: 10
      loss:
        _target_: torch.nn.BCELoss
      optimizer:
        lr: 0.001
        name: SGD
        weight_decay: 0.01
      scheduler:
        gamma: 1
        name: StepLR
        step_size: 1

    model:
      _target_: armlet.utils.net.LogRegression
      input_size: 99
      num_classes: 1

    server:
      loss:
        _target_: torch.nn.BCELoss
      time_to_accuracy_target: null
      weighted: true

eval:
  _target_: armlet.eval.evaluators.MultiCriteriaBinaryClassEval
  eval_every: 1
  locals: true
  metrics:
    fairness: armlet.eval.metrics.BinaryFairnessMetrics
  post_fit: true
  pre_fit: true
  server: true

logger:
  _target_: armlet.utils.log.ArmletLog
  json_log_dir: ${paths.output_dir}

save: {}