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Robust Decentralized Federated Distillation via Multi-Modality Knowledge Collaboration
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 predict
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