7 papers
Nesterov-Accelerated Robust Federated Learning Over Byzantine Adversaries
Lihan Xu, Yanjie Dong, Gang Wang +3
We investigate robust federated learning, where a group of workers collaboratively train a shared model under the orchestration of a central server in the presence of Byzantine adv…
FedSM: Robust Semantics-Guided Feature Mixup for Bias Reduction in Federated Learning with Long-Tail Data
Jingrui Zhang, Yimeng Xu, Shujie Li +5
Federated Learning (FL) enables collaborative model training across decentralized clients without sharing private data. However, FL suffers from biased global models due to non-IID…
CO-PFL: Contribution-Oriented Personalized Federated Learning for Heterogeneous Networks
Ke Xing, Yanjie Dong, Xiaoyi Fan +4
Personalized federated learning (PFL) addresses a critical challenge of collaboratively training customized models for clients with heterogeneous and scarce local data. Conventiona…
Exploring and Mitigating Fawning Hallucinations in Large Language Models
Zixuan Shangguan, Yanjie Dong, Lanjun Wang +3
Large language models (LLMs) have demonstrated exceptional proficiency in language understanding. However, when LLMs align their outputs with deceptive and/or misleading prompts, t…
OVG-HQ: Online Video Grounding with Hybrid-modal Queries
Runhao Zeng, Jiaqi Mao, Minghao Lai +5
Video grounding (VG) task focuses on locating specific moments in a video based on a query, usually in text form. However, traditional VG struggles with some scenarios like streami…
Model Splitting Enhanced Communication-Efficient Federated Learning for CSI Feedback
Yanjie Dong, Haijun Zhang, Gaojie Chen +3
Recent advancements have introduced federated machine learning-based channel state information (CSI) compression before the user equipments (UEs) upload the downlink CSI to the bas…