15 papers
From Selection to Scheduling: Federated Geometry-Aware Correction Makes Exemplar Replay Work Better under Continual Dynamic Heterogeneity
Zhuang Qi, Ying-Peng Tang, Lei Meng +4
Exemplar replay has become an effective strategy for mitigating catastrophic forgetting in federated continual learning (FCL) by retaining representative samples from past tasks. E…
Class-wise Balancing Data Replay for Federated Class-Incremental Learning
Zhuang Qi, Ying-Peng Tang, Lei Meng +3
Federated Class Incremental Learning (FCIL) aims to collaboratively process continuously increasing incoming tasks across multiple clients. Among various approaches, data replay ha…
Large model retrieval enhancement framework for construction site risk identification
Jiawei Li, Chengye Yang, Yaochen Zhang +3
This study addresses construction site hazard identification by proposing a retrieval-augmented framework that enhances large language models (LLMs) without requiring fine-tuning.…
Global Prompt Refinement with Non-Interfering Attention Masking for One-Shot Federated Learning
Zhuang Qi, Pan Yu, Lei Meng +4
Federated Prompt Learning (FPL) enables communication-efficient adaptation by tuning lightweight prompts on top of frozen pre-trained models. Existing FPL methods typically rely on…
Federated Cross-Training Learners for Robust Generalization under Data Heterogeneity
Zhuang Qi, Lei Meng, Ruohan Zhang +5
Federated learning benefits from cross-training strategies, which enables models to train on data from distinct sources to improve generalization capability. However, due to inhere…
Negative Sampling in Recommendation: A Survey and Future Directions
Haokai Ma, Ruobing Xie, Lei Meng +5
Recommender system (RS) aims to capture personalized preferences from massive user behaviors, making them pivotal in the era of information explosion. However, the presence of ``in…