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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.11571 [cs.LG] |
| (or arXiv:2605.11571v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.11571 arXiv-issued DOI via DataCite (pending registration) |
From: Alberto Fernández-Hernández [view email]
[v1]
Tue, 12 May 2026 05:59:23 UTC (168 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。