FedDCOL：Federated Dynamic Continual Oversampling Learning for Imbalanced Tabular Data Classification

Federated learning (FL) enables multiple clients to train a global model without sharing raw local data. However, clients usually face the problem of imbalanced class distributions, which may bias the global classifier toward the majority class. Existing data level approaches typically use static oversampling strategies, making it difficult to dynamically adjust minority class generation during federated training. To address this limitation, this study proposes Federated Dynamic Continual Oversampling Learning (FedDCOL). FedDCOL first characterizes the local data structure based on class overlap degree and then partitions local samples into safe, boundary, and noisy regions. Based on this structural information, FedDCOL develops a Structure-Constrained Diffusion Model (SC-Diff). By incorporating a base diffusion objective, a prototype relation constraint and an inter-class margin constraint, SC-Diff learns the data distribution while preserving the discriminative structure of the minority class. During federated training, SC-Diff is continually updated across communication rounds to adapt minority class generation, while structure selection guided by class overlap information retains reliable generated samples for classifier training. This transforms one-shot oversampling into a dynamic continual oversampling process. Experimental results on seven real world imbalanced tabular datasets show that FedDCOL outperforms six SOTA methods, achieving improvements of $2.23\%$, $12.41\%$, and $10.39\%$ in AUC, F1-score, and G-mean over the strongest baselines, respectively. The particularly pronounced gains in F1-score and G-mean highlight the effectiveness of dynamic continual oversampling for minority class learning in federated tabular classification.