Joint Domain-Class Modeling for Federated Learning Under Feature Skew

Federated Domain Adaptation (FDA) improves model performance at a target client by collaborating with source clients while preserving data privacy. FDA faces two key challenges: domain shift between source and target data, and limited labeled data at the target, a common constraint when a new site joins a federation before it has accumulated its own labeled data, as in clinical deployments. Most existing methods address domain shift alone, assuming ample target data; those that also tackle data scarcity still fail to prioritize source information according to the target's specific objective. We propose FedDAF, which addresses both challenges through similarity-based aggregation of the global source and target models, using their model functional distance, computed from the angle between their mean gradient fields on target data and normalized via a Gompertz function. The global source model itself is formed using a distance-based weighted average, giving greater weight to source models closer to the target model. Experiments on real-world datasets show FedDAF outperforms existing federated learning (FL), personalized FL, and FDA methods in test accuracy.

Federated learning enables privacy-preserving collaboration across distributed devices without centralizing local data. However, clients may differ not only in data distributions but also in domain knowledge and annotation capabilities. In this paper, we introduce label granularity skew, a new form of statistical heterogeneity in federated hierarchical classification, in which clients provide taxonomy-consistent labels at different levels of detail within a shared class hierarchy. To model this heterogeneity, we generate client-specific local label hierarchies using a probabilistic relational neighbor classifier and construct a WordNet-guided hierarchy via silhouette score-based coarsening. Our analysis shows that strongly coupled hierarchical models are sensitive to incomplete supervision, while the conditional softmax classifier is more robust. Based on this insight, we propose Branch-wise Decoupled Fine-Tuning (BDFT) and its federated version, FedBDFT, which fine-tune branch-wise classifiers and aggregate them through federated optimization. Experiments on CIFAR-100, TinyImageNet, and ImageNet show that FedBDFT substantially improves robustness under severe label granularity skew, with average gains of 27.9% and 56.4% at skewness levels of 0.6 and 0.9, respectively. Zero-shot results further indicate that FedBDFT better preserves hierarchical representations for unseen fine-grained classes. These findings demonstrate its effectiveness for federated hierarchical classification with heterogeneous label granularities.

Label skew in federated learning (FL) causes client drift and degrades global accuracy. Synthetic data augmentation can reduce this imbalance; however, full class balancing requires substantial computation cost. We propose FedEAS, a policy that assigns each client an entropy-adaptive per-class generation budget computed from its local label distribution. The budget jointly decides \emph{how much} each client generates and \emph{WHERE} the samples go. Accordingly, the total generation budget follows from the per-client budgets rather than being fixed in advance. FedEAS recovers most of the accuracy gain of full class balancing while reducing the generation budget by 94.1%. At the same total generation budget, it outperforms Uniform allocation by up to 18.82% across CIFAR-10 and CIFAR-100.