FedRALA: Federated Test-Time OOD Detection via Reliable Negative-Label Aggregation and Local Adaptation

Vision-language models enable training-free out-of-distribution (OOD) detection by comparing image representations with in-distribution (ID) class prompts and negative textual labels. Recent work dynamically activates negative labels from unlabeled test streams to capture evolving OOD semantics, but mainly considers centralized test streams. In federated settings, OOD samples vary across clients, making the utility of negative labels dependent on local ID semantics and each client's current test distribution. Naive global averaging can suppress strong negative-label activations confined to a few clients and transfer labels that conflict with local ID semantics or respond weakly to local OOD samples. To address these challenges, we formulate federated test-time OOD detection through negative-label collaboration and propose FedRALA. FedRALA explicitly addresses three federation-specific failure modes: sparse cross-client OOD evidence, conflicts between shared negative labels and client-specific ID semantics, and temporal mismatch between historical shared evidence and the current local stream. FedRALA exchanges only prompt-level activation statistics, without sharing raw images or visual features. Experiments across multiple ID benchmarks and heterogeneous federated OOD streams validate its effectiveness. Notably, with TinyImageNet as the ID benchmark, FedRALA reduces the average FPR95 across six OOD datasets from 35.6% to 29.7% under severe client heterogeneity and disjoint OOD streams.