FedOUI: OUI-Guided Client Weighting for Federated Aggregation

2026-05-12

AI Summary

This work proposes FedOUI, a novel federated learning aggregation strategy that addresses the limitation of existing methods—which predominantly rely on data volume or gradient information while neglecting the internal structural organization of client models in input space. FedOUI introduces, for the first time, an unlabeled, lightweight activation-based metric called the Overfitting-Underfitting Index (OUI) into federated aggregation. By employing a fixed probe batch to dynamically assess structural consistency across client models, and integrating round-level OUI distribution estimation with a smoothing-based reweighting mechanism, FedOUI automatically downweights clients exhibiting anomalous structural behavior. The method significantly enhances both robustness and interpretability of model aggregation, consistently outperforming baselines such as FedAvg and FedProx under strong non-IID and noisy conditions on CIFAR-10, with particularly pronounced gains in highly heterogeneous settings.

Abstract

Federated learning usually aggregates client updates using dataset size or gradient-level criteria, while overlooking internal signals about how each client model is organizing its input space during training. We introduce FedOUI, a simple aggregation rule based on the Overfitting-Underfitting Indicator (OUI), an activation-based and label-free metric. Each participating client sends its local update together with a OUI value computed on a fixed probe batch, and the server estimates the round-wise OUI distribution to assign lower weights to structurally atypical clients through a smooth reweighting rule. We evaluate FedOUI on CIFAR-10 under strong non-IID partitioning and noisy-client conditions, comparing it with FedAvg, FedProx, and a gradient-alignment baseline. The clearest gains appear under strong heterogeneity, where OUI-based weighting improves aggregation quality while remaining lightweight and interpretable. These results show that internal activation structure can provide useful information for federated aggregation beyond client size and gradient geometry.

Problem

• federated learning
• client weighting
• non-IID
• aggregation
• model heterogeneity

Innovation

• Federated Learning
• OUI-Guided Aggregation
• Client Weighting
• Activation-Based Metric
• Non-IID