10 papers
Personalized Additive Modeling for Multi-level Federated Learning
Shutong Chen, Guodong Long, Tianyi Zhou +3
Contemporary AI faces the challenge of balancing generality with user-specific personalization. In federated learning (FL), this challenge is amplified by highly heterogeneous clie…
Bi-level Heterogeneous Learning for Time Series Foundation Models: A Federated Learning Approach
Shengchao Chen, Guodong Long, Dikai Liu +1
Heterogeneity in time series data is more pronounced than in vision or language, as temporal dynamics vary substantially across domains and tasks. Existing efforts on training time…
FeDaL: Federated Dataset Learning for General Time Series Foundation Models
Shengchao Chen, Guodong Long, Michael Blumenstein +1
Dataset-level heterogeneity introduces significant domain biases that fundamentally degrade generalization on general Time Series Foundation Models (TSFMs), yet this challenge rema…
FedMerge: Federated Personalization via Model Merging
Shutong Chen, Tianyi Zhou, Guodong Long +2
One global model in federated learning (FL) might not be sufficient to serve many clients with non-IID tasks and distributions. While there has been advances in FL to train multipl…
MIPO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning
Xueping Peng, Guodong Long, Tao Shen +4
Representation learning on electronic health records (EHRs) plays a vital role in downstream medical prediction tasks. Although natural language processing techniques, such as recu…
Raw Data Matters: Enhancing Prompt Tuning by Internal Augmentation on Vision-Language Models
Haoyang Li, Liang Wang, Chao Wang +4
For CLIP-based prompt tuning, introducing more data as additional knowledge for enhancing fine-tuning process is proved to be an effective approach. Existing data amplification str…