Sub-model federated learning allows clients with limited memory and compute to join training by optimising width-reduced slices of a shared global network. Established schemes such as HeteroFL and FjORD size these slices from device resources alone, and a growing line of adaptive methods proposes to also enlarge the slices of clients whose data deviate most from the population, inferring that deviation from the model updates the server already receives. This paper puts that design pattern to a controlled test. We construct HAS-FL, a representative heterogeneity-aware allocation framework that isolates this inference step, and evaluate it on CIFAR-10, EMNIST and the naturally partitioned Shakespeare corpus. We find that the inference step fails: divergence between local and global updates tracks the width of each client's sub-model, with a strong negative correlation in every image run, and carries little or no information about ground-truth label skew once width is held fixed. We also identify a separate failure of capped allocation: weights that no client trains remain at their random initial values yet still enter every forward pass, driving accuracy close to chance on the 62-class EMNIST task; guaranteeing that every parameter is trained by at least one client removes this failure. Finally, a random allocation with the same average budget matches the adaptive policy on the image tasks and surpasses it on text. The gains attributed to adaptive allocation therefore come from capacity budgeting and parameter coverage rather than heterogeneity estimation, and we recommend capacity-stratified validation and matched-budget random baselines as standard practice.

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