I’ve worked at companies with async training. Async training does help on fault tolerance and also can assist with training thoroughput by being less reliant on slowest machine. It does add meaningful training noise and when we did experiments against sync training we got much more stable results with sync training and some of our less stable models would even sometimes have loss explosions/divergence issues with async training but be fine with sync training.

Although even for async training generally I see dataset just sharded and if worker goes down then shard of data may be loss/skipped not some kind of smarter dynamic file assignment factoring when workers go down. Even basic things like job fails continue from last checkpoint with same dataset state for large epoch is messy when major libraries like tensorflow lack a good dataset checkpointing mechanism.