Which of the following descriptions of distributed training is wrong?

In data parallelism, each computing node trains the complete model based on local data

Distributed training can support the training of massive data and complex models

Data parallelism and model parallelism are common distributed training methods

In model parallelism, each compute node will only interact with the trained sub-model and obtain the final model through model aggregation