How do you ensure clustering consistency across distributed training runs?

Consistency is ensured using deterministic initialization, fixed seeds, and synchronized aggregation.

Distributed clustering suffers from non-determinism due to parallel execution order and floating-point differences. To ensure reproducibility, systems enforce fixed random seeds, deterministic partitioning, and synchronized centroid updates. Some systems also enforce ordered reduction steps during aggregation to reduce variance.