arXiv AI By Itai David, Daphna Weinshall

Follow the Geometry, Not the Model: Cold Start Semi-Supervised Learning

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arXiv AI
Aug 25

FedCC: Towards Addressing Label Distribution Skews in Distillation-Based Federated Learning

FedCC is a new algorithm for distillation-based federated learning that tackles label distribution skew by allowing clients to mark ambiguous samples as 'unknown' instead of forcing a potentially wrong classification. By adding this extra class and calibrating pseudo-labels on a public dataset, FedCC balances confidence across majority and minority classes. Experiments show that FedCC outperforms existing methods, achieving 67.3% accuracy even when each client has data from only one of ten classes, whereas baselines drop to near-random performance.

By Wenxuan Ye, Onur Ayan, Xueli An, Georg Carle
arXiv Machine Learning
Aug 27

JEPAMatch: Geometric Representation Shaping for Semi-Supervised Learning

JEPAMatch introduces a new semi‑supervised learning framework that replaces traditional output‑thresholding with explicit geometric shaping of latent representations. By combining the FlexMatch loss with a latent‑space regularization inspired by LeJEPA, the method encourages isotropic Gaussian structure in the embedding space, mitigating class imbalance and noisy pseudo‑labels. Experiments on CIFAR‑100, STL‑10, and Tiny‑ImageNet show consistent performance gains and faster convergence compared to existing FixMatch‑based baselines.

By Ali Aghababaei-Harandi, Aude Sportisse, Massih-Reza Amini