Personalized Federated Learning (PFL) aims to learn multiple task-specific models rather than a single global model across heterogeneous data distributions. Existing PFL approaches typically rely on iterative optimization-such as model update trajectories-to cluster users that need to accomplish the same tasks together. However, these learning-dynamics-based methods are inherently vulnerable to low-quality data and noisy labels, as corrupted updates distort clustering decisions and degrade personalization performance. To tackle this, we propose FB-NLL, a feature-centric framework that decouples user clustering from iterative training dynamics. By exploiting the intrinsic heterogeneity of local feature spaces, FB-NLL characterizes each user through the spectral structure of the covariances of their feature representations and leverages subspace similarity to identify task-consistent user groupings. This geometry-aware clustering is label-agnostic and is performed in a one-shot manner prior to training, significantly reducing communication overhead and computational costs compared to iterative baselines. Complementing this, we introduce a feature-consistency-based detection and correction strategy to address noisy labels within clusters. By leveraging directional alignment in the learned feature space and assigning labels based on class-specific feature subspaces, our method mitigates corrupted supervision without requiring estimation of stochastic noise transition matrices. In addition, FB-NLL is model-independent and integrates seamlessly with existing noise-robust training techniques. Extensive experiments across diverse datasets and noise regimes demonstrate that our framework consistently outperforms state-of-the-art baselines in terms of average accuracy and performance stability.

翻译：个性化联邦学习（PFL）旨在跨异构数据分布学习多个任务特定模型，而非单一全局模型。现有PFL方法通常依赖迭代优化（如模型更新轨迹）来聚类需要共同完成相同任务的用户。然而，这些基于学习动态的方法本质上易受低质量数据和噪声标签的影响，因为损坏的更新会扭曲聚类决策并降低个性化性能。为解决此问题，我们提出FB-NLL，一种以特征为中心的框架，将用户聚类与迭代训练动态解耦。通过利用局部特征空间的固有异构性，FB-NLL通过特征表示协方差的谱结构来刻画每个用户，并利用子空间相似性识别任务一致的用户分组。这种几何感知聚类与标签无关，且在训练前以一次性方式执行，与迭代基线方法相比显著降低了通信开销和计算成本。作为补充，我们引入了一种基于特征一致性的检测与校正策略来处理聚类内的噪声标签。通过利用学习特征空间中的方向对齐并根据类别特定特征子空间分配标签，我们的方法无需估计随机噪声转移矩阵即可缓解损坏的监督信号。此外，FB-NLL与模型无关，并能无缝集成现有的噪声鲁棒训练技术。跨多种数据集和噪声机制的广泛实验表明，我们的框架在平均准确率和性能稳定性方面持续优于最先进的基线方法。