What is ensemble learning in distributed machine learning systems?

Ensemble learning in distributed systems trains models across multiple nodes and aggregates predictions for scalability and robustness.

In distributed ML systems, datasets are often too large to fit on a single machine. Ensemble learning naturally fits this setup by training base models independently on different partitions of data or across different nodes. The predictions are then aggregated centrally or in a hierarchical fashion. This improves scalability, fault tolerance, and sometimes even generalization because each node may capture slightly different data distributions. However, care must be taken to avoid data skew and ensure consistent model evaluation.