def METHOD_NAME(self, data_instances, validate_data=None):
    """
    Train poisson regression model of role host
    Parameters
    ----------
    data_instances: Table of Instance, input data
    """
    LOGGER.info("Enter hetero_poisson host")
    # self._abnormal_detection(data_instances)
    # self.validation_strategy = self.init_validation_strategy(data_instances, validate_data)
    # self.header = self.get_header(data_instances)
    self.prepare_fit(data_instances, validate_data)
    self.callback_list.on_train_begin(data_instances, validate_data)
    self.cipher_operator = self.cipher.gen_paillier_cipher_operator()
    self.batch_generator.initialize_batch_generator(data_instances)
    LOGGER.info("Start initialize model.")
    model_shape = self.get_features_shape(data_instances)
    if self.init_param_obj.fit_intercept:
        self.init_param_obj.fit_intercept = False
    if not self.component_properties.is_warm_start:
        w = self.initializer.init_model(model_shape, init_params=self.init_param_obj)
        self.model_weights = LinearModelWeights(w, fit_intercept=self.fit_intercept, raise_overflow_error=False)
    else:
        self.callback_warm_start_init_iter(self.n_iter_)
    while self.n_iter_ < self.max_iter:
        LOGGER.info("iter:" + str(self.n_iter_))
        batch_data_generator = self.batch_generator.generate_batch_data()
        self.optimizer.set_iters(self.n_iter_)
        batch_index = 0
        for batch_data in batch_data_generator:
            self.callback_list.on_epoch_begin(self.n_iter_)
            LOGGER.info("iter:" + str(self.n_iter_))
            optim_host_gradient = self.gradient_loss_operator.compute_gradient_procedure(
                batch_data,
                self.cipher_operator,
                self.model_weights,
                self.optimizer,
                self.n_iter_,
                batch_index)
            self.gradient_loss_operator.compute_loss(batch_data, self.model_weights,
                                                     self.optimizer,
                                                     self.n_iter_, batch_index, self.cipher_operator)
            self.model_weights = self.optimizer.update_model(self.model_weights, optim_host_gradient)
            batch_index += 1
        self.is_converged = self.converge_procedure.sync_converge_info(suffix=(self.n_iter_,))
        LOGGER.info("Get is_converged flag from arbiter:{}".format(self.is_converged))
        self.callback_list.on_epoch_end(self.n_iter_)
        self.n_iter_ += 1
        if self.stop_training:
            break
        LOGGER.info("iter: {}, is_converged: {}".format(self.n_iter_, self.is_converged))
        if self.is_converged:
            break
    self.callback_list.on_train_end()
    self.set_summary(self.get_model_summary())