How does TensorFlow handle inconsistent gradient updates in asynchronous distributed training?

Asynchronous training leads to stale gradients because workers update parameters independently.

In async training (e.g., parameter server architecture), workers compute gradients on outdated model weights. These stale gradients are applied to newer parameters, causing instability and slower convergence. TensorFlow allows async training but it trades accuracy consistency for speed. This is why synchronous training is preferred in modern setups.