Who to Trust? Aggregating Client Predictions in Federated Distillation

ArXi:2509.15147v2 Announce Type: replace Under data heterogeneity (e.g., $\textit{class mismatch}$), clients may produce unreliable predictions for instances belonging to unfamiliar classes. An equally weighted combination of such predictions can corrupt the teacher signal used for distillation. In this paper, we provide a theoretical analysis of Federated Distillation and show that aggregating client predictions on a shared public dataset converges to a neighborhood of the optimum, where the neighborhood size is governed by the aggregation quality.