SOURCE-LINKED INTELLIGENCE

Robust Decentralized Federated Distillation via Multi-Modality Knowledge Collaboration

arXiv · AI, language, vision and robotics · article · Sep 7, 2026 · UTC

This paper propose a robust decentralized federated distillation method that enables clients with heterogeneous models to collaborate through predictions on shared unlabeled public data. In the proposed method, each client first evaluates the received predictions in three modalities of class prediction, boundary decision, and prediction correlation. It then filters unreliable clients, assigns reliability-based weights to the retained clients, and constructs a teacher for each type of knowledge. Finally, the corresponding distillation gradients are validated using a supervised gradient computed

Read original source ↗ Open in workspace

recordType

paper

region

Global