Not Every Sync Is Safe: Calibrated DiLoCo Scheduling for Shared AI Infrastructure
DiLoCo-style training reduces communication by letting learner islands train locally before occasional outer synchronization, making it attractive for fragmented industrial AI fleets where training shares hardware with latency-sensitive serving. The question for such fleets is when an outer merge is worth its system cost, and whether choosing \emph{which} windows to defer matters at all. Existing scheduling studies evaluate workload-aware policies against fixed-period baselines, but most omit the control that isolates timing from budget: matched random deferral, which inherits the controller's synchronization budget but is not itself deployable. This omission is consequential: across cont...
| Authors | Maxwell Twelftree, David Lemphers, An-chi He et al. |
|---|---|
| Primary category | cs.DC |
| Published | 24 June 2026 |
| Last updated at source | 24 June 2026 |
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