Titlebook: PRICAI 2024: Trends in Artificial Intelligence; 21st Pacific Rim Int Rafik Hadfi,Patricia Anthony,Quan Bai Conference proceedings 2025 The

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Personalized Federated Learning with Feature Alignment via Knowledge Distillationis a typical approach to PFL. It decouples the model into a feature extractor and a classifier head, where the feature extractor is trained collaboratively to learn a common representation and the classifier head is personalized for local data. Since local training only learns personalized feature i

PHON

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Preserving Individual User’s Right to Be Forgotten in Enterprise-Level Federated Learningir personal data from information service providers. While some prior studies have explored the problem of removing a client’s contribution in federated learning, there is a dearth of research considering the unlearning from the user’s perspective. In enterprise-level federated learning, a company p

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Contrastive Prototype Network for Generative Zero-Shot Learningtraints for unseen class visual features. To address this, we propose the Contrastive Prototype Network (CPNet). CPNet uses prototype learning to determine the feature vector center for each category (the prototype) and classifies based on the similarity between test data and prototypes. Concurrentl

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Conference proceedings 2025 Intelligence, PRICAI 2024, held in Kyoto, Japan, in November 18–24, 2024...The 145 full papers and 35 short papers included in this book were carefully reviewed and selected from 543 submissions. ..The papers are organized in the following topical sections:..Part I: Machine Learning, Deep Learning.