Distributed learning systems typically assume that local data is already available at clients with fixed quality, while in practice, data is sensed through imperfect physical processes whose quality depends on modality, resolution, sensing power, and sample size. We model sensing noise as a structured, modality-dependent covariance and derive a non-convex learning convergence bound whose irreducible sensing floor is governed by the alignment between the modality noise covariance and the loss-sensitivity geometry. Thus, the optimal modality minimizes this noise-gradient alignment rather than total noise power alone. The analysis further yields a sensor-hardware achievability bound for epsilon-stationarity and a hardware-saturation threshold on the accumulated dataset size. We jointly optimize modality, resolution, power, and sample count and demonstrate the performance gain through simulations.

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