5 papers
When Model Editing Meets Service Evolution: A Knowledge-Update Perspective for Service Recommendation
Guodong Fan, Cuiyun Gao, Chun Yong Chong +4
The rapid evolution of software services poses substantial challenges to the design and implementation of effective recommendation systems. Traditional service recommendation appro…
RefProtoFL: Communication-Efficient Federated Learning via External-Referenced Prototype Alignment
Hongyue Wu, Hangyu Li, Guodong Fan +3
Federated learning (FL) enables collaborative model training without sharing raw data in edge environments, but is constrained by limited communication bandwidth and heterogeneous…
A Systematic Literature Review of Code Hallucinations in LLMs: Characterization, Mitigation Methods, Challenges, and Future Directions for Reliable AI
Cuiyun Gao, Guodong Fan, Chun Yong Chong +5
Model hallucination is one of the most critical challenges faced by Large Language Models (LLMs), especially in high-stakes code intelligence tasks. As LLMs become increasingly int…
Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices
Hangyu Li, Hongyue Wu, Guodong Fan +3
As privacy protection gains increasing importance, more models are being trained on edge devices and subsequently merged into the central server through Federated Learning (FL). Ho…
FairSort: Learning to Fair Rank for Personalized Recommendations in Two-Sided Platforms
Guoli Wu, Zhiyong Feng, Shizhan Chen +6
Traditional recommendation systems focus on maximizing user satisfaction by suggesting their favourite items. This user-centric approach may lead to unfair exposure distribution am…