ATCG adaptively gates gradient evaluations in continuous greedy via progress-ratio thresholds to reduce communication while providing a curvature-dependent approximation guarantee that recovers full CG performance in low-curvature regimes.
Communication-efficient learning of deep networks from decentralized data
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Adaptive Threshold-Driven Continuous Greedy Method for Scalable Submodular Optimization
ATCG adaptively gates gradient evaluations in continuous greedy via progress-ratio thresholds to reduce communication while providing a curvature-dependent approximation guarantee that recovers full CG performance in low-curvature regimes.