🤝 Federated Learning

🎞️ ECCV2026 · 2 paper notes

📌 Same area in other venues: 📷 CVPR2026 (19)

🔥 Top topics: Federated Learning ×2

Neural Collapse-Inspired Multi-Label Federated Learning under Label-Distribution Skew

FedNCA-ML extracts class-wise image features and aligns their geometry across clients using a shared fixed equiangular tight frame and positive and negative feature regularization, raising macro-AUC from the strongest comparator's 83.63 to 87.55 in one skewed multi-label CIFAR-10 setting, without improving every micro metric.

Prevention over Correction: Learning Aligned Representations in One-shot Federated Learning

FACE moves cross-client representation alignment into local training through shared fixed class directions and an anti-collapse regularizer, allowing feature fusion without server-side recalibration and improving CIFAR-10 accuracy from the strongest comparator's 60.28% to 69.21% under extreme heterogeneity.