Public articles linked to the same research event.
arXiv The work first links data augmentation to federated learning convergence behavior, showing that reducing label proportion imbalance accelerates convergence, and then proposes DAFL: with clients' class counts known, the server assigns the minimum augmentation per client-class pair by minimizing augmentation and training time while constraining global class imbalance; on six imbalanced configurations of CIFAR-10 with eight classes and eight clients, DAFL achieves higher minority-class F1 with shorter training time on D2, D4, D5, and D6, reaching minority-class F1 0.183±0.055 and convergence time 188.119±86.344 s on D2, outperforming FedProx and Focal Loss.
The work first links data augmentation to federated learning convergence behavior, showing that reducing label proportion imbalance accelerates convergence, and then proposes DAFL: with clients' class counts known, the server assigns the minimum augmentation per client-class pair by minimizing augmentation and training time while constraining global class imbalance; on six imbalanced configurations of CIFAR-10 with eight classes and eight clients, DAFL achieves higher minority-class F1 with shorter training time on D2, D4, D5, and D6, reaching minority-class F1 0.183±0.055 and convergence time 188.119±86.344 s on D2, outperforming FedProx and Focal Loss.
The work first links data augmentation to federated learning convergence behavior, showing that reducing label proportion imbalance accelerates convergence, and then proposes DAFL: with clients' class counts known, the server assigns the minimum augmentation per client-class pair by minimizing augmentation and training time while constraining global class imbalance; on six imbalanced configurations of CIFAR-10 with eight classes and eight clients, DAFL achieves higher minority-class F1 with shorter training time on D2, D4, D5, and D6, reaching minority-class F1 0.183±0.055 and convergence time 188.119±86.344 s on D2, outperforming FedProx and Focal Loss.
The work first links data augmentation to federated learning convergence behavior, showing that reducing label proportion imbalance accelerates convergence, and then proposes DAFL: with clients' class counts known, the server assigns the minimum augmentation per client-class pair by minimizing augmentation and training time while constraining global class imbalance; on six imbalanced configurations of CIFAR-10 with eight classes and eight clients, DAFL achieves higher minority-class F1 with shorter training time on D2, D4, D5, and D6, reaching minority-class F1 0.183±0.055 and convergence time 188.119±86.344 s on D2, outperforming FedProx and Focal Loss.