Semantic-based Distributed Learning for Diverse and Discriminative Representations

ArXi:2604.18237v1 Announce Type: new In large-scale distributed scenarios, increasingly complex tasks demand intelligent collaboration across networks, requiring the joint extraction of structural representations from data samples. However, conventional task-specific approaches often result in nonstructural embeddings, leading to collapsed variability among data samples within the same class, particularly in classification tasks.