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20212025
most citedEmerging Trends in Federated Learning: From Model Fusion to Federated X Learning

108 citations · 125 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2025★ 3 cited

DrugPilot: LLM-based Parameterized Reasoning Agent for Drug Discovery

Kun Li, Zhennan Wu, Shoupeng Wang +3

Large language models (LLMs) integrated with autonomous agents hold significant potential for advancing scientific discovery through automated reasoning and task execution. However…

cs.CR2025

A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment

Kun Wang, Guibin Zhang, Zhenhong Zhou +100

The remarkable success of Large Language Models (LLMs) has illuminated a promising pathway toward achieving Artificial General Intelligence for both academic and industrial communi…

cs.SI2024★ 11 cited

Balancing User Preferences by Social Networks: A Condition-Guided Social Recommendation Model for Mitigating Popularity Bias

Xin He, Wenqi Fan, Ruobing Wang +4

Social recommendation models weave social interactions into their design to provide uniquely personalized recommendation results for users. However, social networks not only amplif…

cs.LG2023★ 3 cited

Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting

Ming Jin, Guangsi Shi, Yuan-Fang Li +7

Time series forecasting has remained a focal point due to its vital applications in sectors such as energy management and transportation planning. Spectral-temporal graph neural ne…

cs.LG2021★ 108 cited

Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning

Shaoxiong Ji, Yue Tan, Teemu Saravirta +6

Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation. As a flexible learning setting, f…