Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learning
Sub-model federated learning lets resource-constrained clients train width-reduced versions of a global model, but existing methods allocate capacity by device resources alone. A natural next step, allocating capacity by each client's data heterogeneity as estimated from the updates the server already observes, is suggested by recent methods that size sub-models from training-derived signals. We ask whether that step is possible, using HAS-FL, an adaptive capacity-allocation framework, as a test case. First, validated against ground-truth label-distribution divergence on reproducible partitions, update-divergence estimates of client heterogeneity are dominated by capacity rather than data...
| Authors | Alireza Moayedikia |
|---|---|
| Primary category | cs.LG |
| Published | 7 August 2026 |
| Last updated at source | 8 October 2026 |
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