用激活特征判断客户端模型状态，优化联邦聚合效果。
FedOUI: OUI-Guided Client Weighting for Federated Aggregation
- 基于激活统计的OUI指标，无须标签即可评估模型过拟合/欠拟合状态。
- 在强非独立同分布下，相比基准方法准确率提升2.3%以上。
- 轻量可解释，适合存在异常客户端的现实联邦学习场景。
联邦学习通常依据数据集规模或梯度特征进行客户端更新聚合，却忽略了客户端模型在训练过程中对输入空间的内部组织信号。本文提出FedOUI，一种基于过拟合-欠拟合指示器（OUI）的简单聚合规则，OUI是一种基于激活且无需标签的度量。每个参与客户端将本地更新连同固定探针批次计算出的OUI值一并上传，服务器估计每轮的OUI分布，并通过平滑重加权规则为结构异常客户端分配更低权重。我们在强非独立同分布划分和噪声客户端条件下于CIFAR-10上评估了FedOUI，对比了FedAvg、FedProx及梯度对齐基线。在高度异构条件下，基于OUI的加权显著提升了聚合质量，同时保持轻量与可解释性。结果表明，仅凭内部激活结构即可为联邦聚合提供超越客户端规模与梯度几何的信息。
原文摘要 · Abstract (English)
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.
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