FateWithShellmiaoComment/Fate/doc/federatedml_component/linear_regression.md

Federated Linear Regression

Linear Regression(LinR) is a simple statistic model widely used for predicting continuous numbers. FATE provides Heterogeneous Linear Regression(HeteroLinR) and SSHE Linear Regression(HeteroSSHELinR).

Below lists features of HeteroLinR & HeteroSSHELinR models:

Linear Model	Arbiter-less Training	Weighted Training	Multi-Host	Cross Validation	Warm-Start/CheckPoint
Hetero LinR	✗	✓	✓	✓	✓
Hetero SSHELinR	✓	✓	✗	✓	✓

Heterogeneous LinR

HeteroLinR also supports multi-Host training. You can specify multiple hosts in the job configuration file like the provided examples/dsl/v2/hetero_linear_regression.

Here we simplify participants of the federation process into three parties. Party A represents Guest, party B represents Host. Party C, which is also known as “Arbiter,” is a third party that works as coordinator. Party C is responsible for generating private and public keys.

The process of HeteroLinR training is shown below:

A sample alignment process is conducted before training. The sample alignment process identifies overlapping samples in databases of all parties. The federated model is built based on the overlapping samples. The whole sample alignment process is conducted in encryption mode, and so confidential information (e.g. sample ids) will not be leaked.

In the training process, party A and party B each compute the elements needed for final gradients. Arbiter aggregates, calculates, and transfers back the final gradients to corresponding parties. For more details on the secure model-building process, please refer to this paper.

Features

1. L1 & L2 regularization

2. Mini-batch mechanism

3. Five optimization methods:

   • sgd
     gradient descent with arbitrary batch size

   • rmsprop
     RMSProp

   • adam
     Adam

   • adagrad
     AdaGrad

   • nesterov_momentum_sgd
     Nesterov Momentum

   • stochastic quansi-newton
     The algorithm details can refer to this paper.

4. Three converge criteria:

   • diff
     Use difference of loss between two iterations, not available for multi-host training

   • abs
     Use the absolute value of loss

   • weight_diff
     Use difference of model weights

5. Support multi-host modeling task. For details on how to configure for multi-host modeling task, please refer to this guide

6. Support validation for every arbitrary iterations

7. Learning rate decay mechanism

8. Support early stopping mechanism, which checks for performance change on specified metrics over training rounds. Early stopping is triggered when no improvement is found at early stopping rounds.

9. Support sparse format data as input.

10. Support stepwise. For details on stepwise mode, please refer to stepwise .

Hetero-SSHE-LinR features:

1. L1 & L2 regularization

2. Mini-batch mechanism

3. Support different encrypt-mode to balance speed and security

4. Support early-stopping mechanism

5. Support setting arbitrary metrics for validation during training

6. Support model encryption with host model