62 research outputs found
NRPA: Neural Recommendation with Personalized Attention
Existing review-based recommendation methods usually use the same model to
learn the representations of all users/items from reviews posted by users
towards items. However, different users have different preference and different
items have different characteristics. Thus, the same word or similar reviews
may have different informativeness for different users and items. In this paper
we propose a neural recommendation approach with personalized attention to
learn personalized representations of users and items from reviews. We use a
review encoder to learn representations of reviews from words, and a user/item
encoder to learn representations of users or items from reviews. We propose a
personalized attention model, and apply it to both review and user/item
encoders to select different important words and reviews for different
users/items. Experiments on five datasets validate our approach can effectively
improve the performance of neural recommendation.Comment: 4 pages, 4 figure
Selective Knowledge Sharing for Privacy-Preserving Federated Distillation without A Good Teacher
While federated learning is promising for privacy-preserving collaborative
learning without revealing local data, it remains vulnerable to white-box
attacks and struggles to adapt to heterogeneous clients. Federated distillation
(FD), built upon knowledge distillation--an effective technique for
transferring knowledge from a teacher model to student models--emerges as an
alternative paradigm, which provides enhanced privacy guarantees and addresses
model heterogeneity. Nevertheless, challenges arise due to variations in local
data distributions and the absence of a well-trained teacher model, which leads
to misleading and ambiguous knowledge sharing that significantly degrades model
performance. To address these issues, this paper proposes a selective knowledge
sharing mechanism for FD, termed Selective-FD. It includes client-side
selectors and a server-side selector to accurately and precisely identify
knowledge from local and ensemble predictions, respectively. Empirical studies,
backed by theoretical insights, demonstrate that our approach enhances the
generalization capabilities of the FD framework and consistently outperforms
baseline methods. This study presents a promising direction for effective
knowledge transfer in privacy-preserving collaborative learning
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