Reliable Federated Multi-View Learning via Conflict-Aware Evidence Calibration

Federated Multi-View Learning (FedMVL) enables multiple clients with heterogeneous data views to collaboratively train global models without sharing private data. Existing FedMVL algorithms often overlook the reliability of local predictions, as both uncertainty quantification and calibration remain challenging in privacy-preserving federated settings. Heterogeneous sensing devices yield varying view qualities, but view-isolated training further exacerbates inherent bias of local model, inducing unreliable outputs. Without proper calibration, these artifacts cause spurious cross-view conflicts, ultimately undermining the accuracy and reliability of the aggregated global model. To address these issues, we propose a Reliable Federated Multi-View Learning with evidence calibration (FedRMVL-CAL), a FedMVL framework with adaptive evidence fusion. The local calibration scheme harmonizes uncertainty across views, while the fusion mechanism adaptively reconciles conflicting opinions, enhancing robustness in global inference. Theoretical analysis and extensive experiments demonstrate that FedRMVL-CAL achieves superior accuracy and reliability compared to existing approaches, ensuring trustworthy global predictions across heterogeneous views.