Linear Regression(LinR) is a simple statistic model widely used for predicting continuous numbers. FATE provides Heterogeneous Linear Regression(HeteroLinR). HeteroLinR also supports multi-Host training. You can specify multiple hosts in the job configuration file like the provided examples.
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:
Figure 1： Federated HeteroLinR Principle
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.
- L1 & L2 regularization
- Mini-batch mechanism
- Five optimization methods: a) “sgd”: gradient descent with arbitrary batch size b) “rmsprop”: RMSProp c) “adam”: Adam d) “adagrad”: AdaGrad e) “nesterov_momentum_sgd”: Nesterov Momentum
- Three converge criteria: a) "diff": Use difference of loss between two iterations, not available for multi-host training b) "abs": Use the absolute value of loss c) "weight_diff": Use difference of model weights
- Support multi-host modeling task. For details on how to configure for multi-host modeling task, please refer to this guide
- Support validation for every arbitrary iterations
- Learning rate decay mechanism.