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FedRAN is a resource-aware analytic federated continual learning framework that replaces gradient-based updates with compact random feature statistics, achieving high accuracy with significantly lower communication and computation costs.
This paper introduces Bernstein–Schur kernels, a class of nonstationary kernels between shift-invariant and dot-product templates, and provides a random feature construction by sketching the finite modulation and randomizing the completely monotone radial factor. The method yields unbiased estimators with operator-norm bounds controlled by intrinsic dimensions, and experiments validate the approach on a biased kernel example.