✨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

==================================

For more data science resources:
✓ https://xn--r1a.website/DataScienceT

#FederatedLearning #MachineLearning #AI #PrivacyPreservingAI #RepresentationLearning

✨FedPS: Federated data Preprocessing via aggregated Statistics

📝 Summary:
FedPS is a federated data preprocessing framework for collaborative machine learning. It uses aggregated statistics and data-sketching for efficient privacy-preserving data preparation in FL, covering tasks like scaling and imputation.

🔹 Publication Date: Published on Feb 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.10870
• PDF: https://arxiv.org/pdf/2602.10870
• Project Page: https://xuefeng-xu.github.io/fedps.html
• Github: https://github.com/xuefeng-xu/fedps

==================================

For more data science resources:
✓ https://xn--r1a.website/DataScienceT

#FederatedLearning #DataPreprocessing #MachineLearning #PrivacyPreservingAI #DataScience