Graph neural networks (GNNs) have attracted extensive research attention in recent years
due to their capability to progress with graph data and have been widely used in practical …

Due to underlying privacy-sensitive information in user-item interaction data, the risk of
privacy leakage exists in the centralized-training recommender system (RecSys). To this …

Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Foundation models have emerged as critical components in a variety of artificial intelligence
applications, and showcase significant success in natural language processing and several …

As data are increasingly being stored in different silos and societies becoming more aware
of data privacy issues, the traditional centralized training of artificial intelligence (AI) models …

Unsorted retired batteries with varied cathode materials hinder the adoption of direct
recycling due to their cathode-specific nature. The surge in retired batteries necessitates …

Subgraphs of a larger global graph may be distributed across multiple devices, and only
locally accessible due to privacy restrictions, although there may be links between …

Collaborative filtering (CF) based recommender systems are typically trained based on
personal interaction data (eg, clicks and purchases) that could be naturally represented as …

Recommender systems have been widely deployed in various real-world applications to
help users identify content of interest from massive amounts of information. Traditional …

As a way to implement the" right to be forgotten" in machine learning, machine unlearning
aims to completely remove the contributions and information of the samples to be deleted …