Database optimization has long relied on traditional methods that struggle with the complexities of modern data environments. These methods often fail to efficiently handle large-scale data, complex queries, and dynamic workloads, leading to suboptimal performance and increased computational costs. To address these challenges, researchers have turned to AI4DB (Artificial Intelligence for Database), integrating advanced machine learning and deep learning techniques to enhance database optimization.

Federated Learning (FL) allows for privacy-preserving model training by enabling clients to upload model gradients without exposing their personal data. However, the decentralized nature of FL introduces vulnerabilities to various attacks, such as poisoning attacks, where adversaries manipulate data or model updates to degrade performance. While current defenses often focus on detecting anomalous updates, they struggle with long-term attack dynamics, compromised privacy, and the underutilization of historical gradient data.

Researchers from Beihang University have conducted a comprehensive bibliometric analysis to identify evolving trends and challenges in evaluating research talent at Chinese universities.

Entity resolution (ER) aims to identify and match records referring to the same entity from multiple data sources, which is a crucial task in data integration. Traditional methods rely on structured data and require extensive manual labeling for better performance, limiting their effectiveness for long-text, unstructured data scenarios, while directly apply LLM for ER occurs with hallucination results with factual error.

Mobile CrowdSensing (MCS) has become a powerful sensing paradigm for information collection recently. As sensing becomes more complicated, it is beneficial to deploy edge servers between users and the cloud center with a so-called mobile edge computing.