From Centralized Learning to Federated Setting: Keeping Reliability on Track

Point-wise reliability assessment helps determine how much confidence can be placed in individual predictions of machine learning models. Most existing studies focus on centralized learning (CL), where the full dataset is accessible for estimating reliability. In federated learning (FL), however, data remain distributed across clients and cannot be shared due to privacy constraints, making many traditional reliability estimators difficult to apply. This work adapts three representative reliability assessment paradigms to federated settings using data obfuscation approaches that avoid sharing raw data. The proposed variants: FedRel-GMM, FedRel-RHH, and FedRel-Emb are evaluated on real-world healthcare data. Experimental results show that FedRel-GMM and FedRel-RHH preserve the reliability trends observed in CL, while the embedding-based method fails to generalize due to shifts in the representation space across clients. These findings clarify which centralized techniques can be used in FL and provide a step toward building reliable distributed systems.