What is distributed model training synchronization strategy?

Synchronization strategies coordinate gradient updates across multiple training nodes.

Distributed training requires synchronizing gradients or parameters across GPUs/servers. Common strategies include synchronous training (all-reduce), asynchronous training (parameter servers), and hybrid approaches. Synchronization impacts convergence speed, fault tolerance, and communication overhead.