Sparse communication in decentralized zeroth-order learning requires compatible peer-state coordinates.
We characterize this one-hop condition and develop \textsf{ZO-COSMO}, coupling two-query estimation with average-preserving masked consensus using $q$ values per active link.
Global supports serve all-neighbor mixing; matching updates require agreement only within each pair.
We derive a sharp contraction-per-scalar bound within the matching class and convergence guarantees for the core and sparse-momentum updates.
At fixed matching, exact moment identities characterize how shared directions preserve gradient-heterogeneity cancellation and redistribute estimation error and disagreement.
Mechanism experiments cover unequal curvatures, noise, and sparse momentum.
Further tests span $64$ synthetic agents and eight logical Qwen LoRA workers.
At matched payload budgets, Qwen2-7B QNLI gains $3.65$ accuracy points over explicit-index Rand-$k$; edge-local updates gain $3.42$ and $2.53$ points over all-neighbor mixing on eight-worker complete and ring graphs.
A matched-first-step ablation gives a $3.92$-point momentum benefit.
Seed-aware and same-matching controls distinguish encoding, scheduling, and query correlation.