README.rst

Homogeneous Neural Networks

Neural networks are probably the most popular machine learning algorithms in recent years. FATE provides a federated homogeneous neural network implementation. We simplified the federation process into three parties. Party A represents Guest，which acts as a task trigger. Party B represents Host, which is almost the same with guest except that Host does not initiate task. Party C serves as a coordinator to aggregate models from guest/hosts and broadcast aggregated model.

Basic Process

As its name suggested, in Homogeneous Neural Networks, the feature spaces of guest and hosts are identical. An optional encryption mode for model is provided. By doing this, no party can get the private model of other parties.

Figure 1 (Federated Homo NN Principle)

The Homo NN process is shown in Figure 1. Models of Party A and Party B have the same neural networks structure. In each iteration, each party trains its model on its own data. After that, all parties upload their encrypted (with random mask) model parameters to arbiter. The arbiter aggregates these parameters to form a federated model parameter, which will then be distributed to all parties for updating their local models. Similar to traditional neural network, the training process will stop when the federated model converges or the whole training process reaches a predefined max-iteration threshold.

Please note that random numbers are carefully generated so that the random numbers of all parties add up an zero matrix and thus disappear automatically. For more detailed explanations, please refer to [Secure Analytics: Federated Learning and Secure Aggregation]. Since there is no model transferred in plaintext, except for the owner of the model, no other party can obtain the real information of the model.

Param

.. automodule:: federatedml.param.homo_nn_param
   :members:

Features

tensorflow backend:

supported layers: Dense: { "layer": "Dense", "units": , "activation": null, "use_bias": true, "kernel_initializer": "glorot_uniform", "bias_initializer": "zeros", "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null } Droupout: { "rate": , "noise_shape": null, "seed": null } other layers listed in tf.keras.layers will be supported in near feature. supported optimizer: all optimizer listed in tf.keras.optimizers Adadelta: adadelta link { "optimizer": "Adadelta", "learning_rate": 0.001, "rho": 0.95, "epsilon": 1e-07 } Adagrad: adagrad link { "optimizer": "Adagrad", "learning_rate": 0.001, "initial_accumulator_value": 0.1, "epsilon": 1e-07 } Adam: adam link { "optimizer": "Adam", "learning_rate": 0.001, "beta_1": 0.9, "beta_2": 0.999, "amsgrad": false, "epsilon": 1e-07 } Ftrl: ftrl link { "optimizer": "Ftrl", "learning_rate": 0.001, "learning_rate_power": -0.5, "initial_accumulator_value": 0.1, "l1_regularization_strength": 0.0, "l2_regularization_strength": 0.0, "l2_shrinkage_regularization_strength": 0.0 } Nadam: nadam link { "optimizer": "Nadam", "learning_rate": 0.001, "beta_1": 0.9, "beta_2": 0.999, "epsilon": 1e-07 } RMSprop: rmsprop link { "optimizer": "RMSprop", "learning_rate": 0.001, "pho": 0.9, "momentum": 0.0, "epsilon": 1e-07, "centered": false } SGD: sgd link { "optimizer": "SGD", "learning_rate": 0.001, "momentum": 0.0, "nesterov": false } supported losses: all losses listed in tf.keras.losses binary_crossentropy categorical_crossentropy categorical_hinge cosine_similarity hinge kullback_leibler_divergence logcosh mean_absolute_error mean_absolute_percentage_error mean_squared_error mean_squared_logarithmic_error poisson sparse_categorical_crossentropy squared_hinge support multi-host: In fact, for model security reasons, at least two host parties are required.

pytorch backend:

There are some difference in nn configuration build by pytorch compared to tf or keras. config_type: pytorch, if use pytorch to build your model nn_define: Each layer is represented as an object in json. supported layers: Linear: { "layer": "Linear", "name": #string, "type": "normal", "config": [input_num,output_num] } other normal layers: BatchNorm2d dropout activate: { "layer": "Relu", "type": "activate", "name": #string } other activate layers: Selu LeakyReLU Tanh Sigmoid Relu Tanh optimizer: A json object is needed "optimizer": { "optimizer": "Adam", "learning_rate": 0.05 } optimizer include "Adam","SGD","RMSprop","Adagrad" loss: A string is needed, supported losses include: "CrossEntropyLoss" "MSELoss" "BCELoss" "BCEWithLogitsLoss" "NLLLoss" "L1Loss" "SmoothL1Loss" "HingeEmbeddingLoss" metrics: A string is needed, supported metrics include: auccuray precision recall auc f1 fbeta

Use

Note

For more information on task configuration, please refer to the [doc] under example first. In this part we only talk about the parameter configuration.

Since all parties training Homogeneous Neural Networks have the same network structure, a common practice is to configure parameters under algorithm_parameters, which is shared across all parties. The basic structure is:

{
      "config_type": "nn",
      "nn_define": [layer1, layer2, ...]
      "batch_size": -1,
      "optimizer": optimizer,
      "early_stop": {
        "early_stop": early_stop_type,
        "eps": 1e-4
      },
      "loss": loss,
      "metrics": [metrics1, metrics2, ...],
      "max_iter": 10
}

Note

Some detailed examples can be found in the example directory

nn_define:	Each layer is represented as an object in json. Please refer to supported layers in Features part.
optimizer:	A json object is needed, please refer to supported optimizers in Features part.
loss:	A string is needed, please refer to supported losses in Features part.
others:	batch_size: a positive integer or -1 for full batch max_iter: max aggregation number, a positive integer early_stop: diff or abs metrics: a string name, refer to [metricsdoc], such as Accuracy, AUC ...