PLGC combines NTK-weighted local-global item embedding mixing with a Barlow Twins-style redundancy reduction loss to lessen embedding degradation in personalized federated recommendation.
Adaptive fair representation learning for personalized fairness in recommendations via information align- ment,
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A Model-agnostic Strategy to Mitigate Embedding Degradation in Personalized Federated Recommendation
PLGC combines NTK-weighted local-global item embedding mixing with a Barlow Twins-style redundancy reduction loss to lessen embedding degradation in personalized federated recommendation.