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Federated Learning with Only Positive Labels by Exploring Label Correlations
Specifically, FedALC estimates the label correlations in the class embedding learning for different label pairs and utilizes it to improve the model training.
Multiple Key-value Strategy in Recommendation Systems Incorporating Large Language Model
Recommendation system (RS) plays significant roles in matching users information needs for Internet applications, and it usually utilizes the vanilla neural network as the backbone to handle embedding details.
FedABC: Targeting Fair Competition in Personalized Federated Learning
In particular, we adopt the ``one-vs-all'' training strategy in each client to alleviate the unfair competition between classes by constructing a personalized binary classification problem for each class.
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