SCOUT: Measuring When Cross-Model Transfer Helps Federated Lightweight Learners

Cross-model transfer can improve lightweight learners, yet the utility of an individual intervention can depend on receiver state and update scale. We study this dependence in federated learning through SCOUT, a receiver-centric matched-intervention protocol comparing a candidate-transfer branch with a matched NoTransfer control from the same pre-intervention state. Across 75 audited states, FL-normalized transfer has positive mean utility but negative observed utility in 21 states. Changing only the update scale reverses utility sign in 29/75 states (38.7%) and makes Fixed-step preferable in 24/75 states (32.0%). A retrospective statewise operator oracle adds $4.88\times 10^{-5}$ mean bidirectional $\Delta \mathrm{MRR}$ over the best fixed operator (22.2%). On 50 canonical states, eight pre-intervention features with Bayesian Ridge and regularized logistic models do not reliably predict FL-normalized utility or harm under seed-grouped held-out evaluation; relative operator preference is not tested. Five matched 20-round repeated-transfer policy pairs have positive final held-out effects, while one-step audits separately contain negative observed utilities. These results distinguish intervention utility, retrospective choice value, single-operator predictability, and trajectory-level outcome.