Spark does not address any of these issues and trains decision trees sequentially on a single node.

Spark uses data sampling techniques to handle data imbalance, minimizes communication overhead through efficient data partitioning, and employs synchronization mechanisms to ensure consistent training across nodes.

Spark only supports linear regression and does not address these issues for decision trees.

Spark relies on a centralized server to handle all data and training, avoiding these issues.