APIs/Parameters

We provide two kinds of APIs: command-line interface (CLI) and Python interface. For CLI, users only need to prepare a configuration file specifying the parameters and call FedTree in a one-line command. For Python interface, users can define two classes FLClassifier and FLRegressor with the parameters and use them in a scikit-learn style (see here). The parameters are below.

Contents

Parameters for Federated Setting

  • mode [default = horizontal, type=string]

    • horizontal: horizontal federated learning

    • vertical: vertical federated learning

  • num_parties [default = 10, type = int, alias: num_clients, num_devices]

    • Number of parties

  • partition [default = 0, type = bool]

    • 0: each party has a prepared local dataset

    • 1: there is a global dataset and users require FedTree to partition it to multiple subsets to simulate federated setting.

  • partition_mode [default=``horizontal``, type=string]

    • horizontal: horizontal data partitioning

    • vertical: vertical data partitioning

  • ip_address [default=``localhost``, type=string, alias: server_ip_address]

    • The ip address of the server in distributed FedTree.

  • data_format [default=``libsvm``, type=string]

    • libsvm: the input data is in a libsvm format (label feature_id1:feature_value1 feature_id2:feature_value2). See here for an example.

    • csv: the input data is in a csv format (the first row is the header and the other rows are feature values). See here for an example.

  • n_features [default=-1, type=int]

    • Number of features of the datasets. It needs to be specified when conducting horizontal FedTree with sparse datasets.

  • propose_split [default=``server``, type=string]

    • server: the server proposes candidate split points according to the range of each feature in horizontal FedTree.

    • party: the parties propose possible split points. Then, the server merge them and sample at most num_max_bin candidate split points in horizontal FedTree.

  • key_length [default=512, type=int]

    • Number of bits of the key used in encryption.

  • pred_output [default=``predictions.txt``, type=string]

    • The file to save the predicted labels when using FedTree-predict

Parameters for GBDTs

  • data [default=``../dataset/test_dataset.txt``, type=string, alias: path]

    • The path to the training dataset(s). In simulation, if multiple datasets need to be loaded where each dataset represents a party, specify the paths seperated with comma.

  • model_path [default=``fedtree.model``, type=string]

    • The path to save/load the model.

  • verbose [default=1, type=int]

    • Printing information: 0 for silence, 1 for key information and 2 for more information.

  • depth [default=6, type=int]

    • The maximum depth of the decision trees. Shallow trees tend to have better generality, and deep trees are more likely to overfit the training data.

  • n_trees [default=40, type=int]

    • The number of training iterations. n_trees equals to the number of trees in GBDTs.

  • max_num_bin [default=32, type=int]

    • The maximum number of bins in a histogram. The value needs to be smaller than 256.

  • learning_rate [default=1, type=float, alias: eta]

    • Valid domain: [0,1]. This option is to set the weight of newly trained tree. Use eta < 1 to mitigate overfitting.

  • objective [default=``reg:linear``, type=string]

    • Valid options include reg:linear, reg:logistic, binary:logistic, multi:softprob, multi:softmax, rank:pairwise and rank:ndcg.

    • reg:linear is for regression, reg:logistic and binary:logistic are for binary classification.

    • multi:softprob and multi:softmax are for multi-class classification. multi:softprob outputs probability for each class, and multi:softmax outputs the label only.

    • rank:pairwise and rank:ndcg are for ranking problems.

  • num_class [default=1, type=int]

    • Set the number of classes in the multi-class classification.

  • min_child_weight [default=1, type=float]

    • The minimum sum of instance weight (measured by the second order derivative) needed in a child node.

  • lambda [default=1, type=float, alias: lambda_tgbm or reg_lambda]

    • L2 regularization term on weights.

  • gamma [default=1, type=float, alias: min_split_loss]

    • The minimum loss reduction required to make a further split on a leaf node of the tree. gamma is used in the pruning stage.

Parameters for Privacy Protection

  • privacy_method [default = none, type=string]

    • none: no additional method is used to protect the communicated messages (raw data is not transferred).

    • he: use homomorphic encryption to protect the communicated messages (for vertical FedTree).

    • sa: use secure aggregation to protect the communicated messages (for horizontal FedTree).

    • dp: use differential privacy to protect the communicated messages (currently only works for vertical FL with single machine simulation).

  • privacy_budget [default=10, type=float]

    • Total privacy budget if using differential privacy.