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.
Federated learning using variance reduced stochastic gradient for probabilistically activated agents
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
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.