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University of Chinese Academy of Sciences
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Gains in semantic recommendation systems hinge more on item-text correspondence than on complex language modeling, challenging assumptions about model superiority.
Federated learning struggles when data quality varies across clients, but FedQual solves this with a novel approach that calibrates low-quality clients while preserving high-quality autonomy.
