177 citations · 631 across the 42 of their papers we have counts for
5 papers · 1 filter
Think Before You Move: Latent Motion Reasoning for Text-to-Motion Generation
Yijie Qian, Juncheng Wang, Yuxiang Feng +7
Current state-of-the-art paradigms predominantly treat Text-to-Motion (T2M) generation as a direct translation problem, mapping symbolic language directly to continuous poses. Whil…
Learning Critically: Selective Self Distillation in Federated Learning on Non-IID Data
Yuting He, Yiqiang Chen, XiaoDong Yang +3
Federated learning (FL) enables multiple clients to collaboratively train a global model while keeping local data decentralized. Data heterogeneity (non-IID) across clients has imp…
FHBench: Towards Efficient and Personalized Federated Learning for Multimodal Healthcare
Penghao Wang, Qian Chen, Teng Zhang +3
Federated Learning (FL) has emerged as an effective solution for multi-institutional collaborations without sharing patient data, offering a range of methods tailored for diverse a…
A Survey on Unlearnable Data
Jiahao Li, Yiqiang Chen, Yunbing Xing +2
Unlearnable data (ULD) has emerged as an innovative defense technique to prevent machine learning models from learning meaningful patterns from specific data, thus protecting data…
Ten Challenging Problems in Federated Foundation Models
Tao Fan, Hanlin Gu, Xuemei Cao +30
Federated Foundation Models (FedFMs) represent a distributed learning paradigm that fuses general competences of foundation models as well as privacy-preserving capabilities of fed…