✨FedRE: A Representation Entanglement Framework for Model-Heterogeneous Federated Learning
📝 Summary:
FedRE is a federated learning framework for model-heterogeneous environments. Clients create and upload entangled representations and entangled-label encodings to train a global classifier. This method enhances performance, protects privacy, and reduces communication overhead.
🔹 Publication Date: Published on Nov 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22265
• PDF: https://arxiv.org/pdf/2511.22265
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✓ https://t.me/DataScienceT
#FederatedLearning #MachineLearning #AI #PrivacyPreservingAI #RepresentationLearning
📝 Summary:
FedRE is a federated learning framework for model-heterogeneous environments. Clients create and upload entangled representations and entangled-label encodings to train a global classifier. This method enhances performance, protects privacy, and reduces communication overhead.
🔹 Publication Date: Published on Nov 27
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22265
• PDF: https://arxiv.org/pdf/2511.22265
==================================
For more data science resources:
✓ https://t.me/DataScienceT
#FederatedLearning #MachineLearning #AI #PrivacyPreservingAI #RepresentationLearning