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Dynamic Feature-Embedding Communication via Codebook Distillation for Federated Recommendation
Federated recommendation systems commonly protect user privacy by keeping user parameters on local devices, while exchanging item parameters for collaborative model training. However, such item parameters usually model items independently and suffer from both efficiency and effectiveness challenges,
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