How does distributed training impact variance and generalization in large-scale ML systems?

Distributed training can reduce variance through data scaling but may introduce optimization instability affecting generalization.

In distributed training, datasets are partitioned across multiple workers, and gradients are aggregated. This allows training on massive datasets, which generally reduces variance by exposing the model to diverse data. However, asynchronous updates or stale gradients can introduce optimization noise, potentially increasing variance in convergence behavior.

Synchronous training improves stability but increases communication overhead. Asynchronous training improves speed but may degrade convergence consistency.…