FedOPhD: Federated Post-Hoc OOD Detection via Unlearning-Inspired Probing

In federated learning (FL), in-distribution (ID) data include both Local-ID (L-ID) from the deployment client and Transferred-ID (T-ID) from other participating clients. Locally unfamiliar T-ID must be distinguished from true out-of-distribution (OOD) inputs, even when only a trained global model and L-ID data are available. We propose FedOPhD, a post-hoc federated OOD detector that probes semantic support through unlearning responses. FedOPhD performs client-conditioned semantic attenuation on a diagnostic model copy, using the client's learned label support to attenuate locally supported semantics. It measures how an input's representation changes align with responses on an L-ID reference subset across multiple layers. These responses offer additional evidence for distinguishing inputs with similar original model prediction confidence. This response evidence is combined with the original model's global recognition support through locally calibrated fusion, accounting for T-ID beyond local semantic coverage. Detector construction requires neither OOD data nor further federated communication, while the deployed classifier remains unchanged. Experiments on CIFAR-10 and CIFAR-100 demonstrate the effectiveness of FedOPhD in distinguishing OOD inputs from both L-ID and T-ID across heterogeneous federated settings.