Data-Free Client Contribution Estimation via Logit Maximization for Federated Learning

Federated learning (FL) enables collaborative learning ofcomputer vision models, where privacy and regulatory constraints pre-vent centralizing data across devices or organizations. However, prac-tical FL deployments often exhibit severe class imbalance and labelskew, causing standard aggregation protocols to overfit dominant clientsand degrade minority-class performance. We propose a data-free, class-wise contribution estimation and aggregation framework based on logitmaximization (CELM) that does not require raw data, client metadata,or auxiliary public datasets at the server. The FL server probes clientupdates to obtain class-wise evidence scores and assembles a cross-clientevidence matrix, which quantifies both per-class competence and classcoverage. Using this matrix, we compute contribution weights that up-weight clients providing discriminative evidence for underrepresentedclasses. The resulting aggregation is stable due to simplex constraints andmomentum smoothing, and remains compatible with standard FL train-ing pipelines. We evaluate the approach on representative vision bench-marks under controlled non-IID and pathological label splits, demon-strating that CELM-based aggregation improves robustness to imbalanceand statistical heterogeneity, while yielding better performance with-out requiring any additional data exchange. The code is available at:https://github.com/asimukaye/celm.