VTR-FL:Verifiable Trust Recovery Against Selection-Censored Feedback in Federated Learning

Reputation-based client selection in federated learning creates an endogenous feedback problem: once a client is excluded, the system may stop observing the very evidence required to determine whether that client has recovered. We formalize this phenomenon as Selection-Censored Trust (**SCT**) and show that, when post-exclusion observation probability collapses to zero, recovered and persistently malicious trajectories become unidentifiable from system-visible history. We introduce Persistent Trust Observability (**PTO**) and propose a framework named **VTR-FL**, which decouples evidence acquisition from model influence. Low-confidence clients remain observable through a verifiable zero-influence channel, while recent certified evidence, not stale historical reputation, determines whether influence can be progressively restored. A post-commit probation mechanism further constrains same-round role-conditioned exploitation. Under the stated verification and cryptographic assumptions, committed observation propensities and state transitions make recovery decisions challengeable. Across CIFAR-10, Fashion-MNIST, and MNIST, VTR-FL substantially increases recovered-client re-entry and model influence relative to reputation-based selection baselines while preserving comparable task accuracy. Adaptive evaluations further characterize the security boundary of controlled re-entry.