12 citations · 25 across the 20 of their papers we have counts for
8 papers · 1 filter
Federated Foundation Models on Heterogeneous Time Series
Shengchao Chen, Guodong Long, Jing Jiang +1
Training a general-purpose time series foundation models with robust generalization capabilities across diverse applications from scratch is still an open challenge. Efforts are pr…
WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents
Siyu Zhou, Tianyi Zhou, Yijun Yang +4
Can large language models (LLMs) directly serve as powerful world models for model-based agents? While the gaps between the prior knowledge of LLMs and the specified environment's…
Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond
Qi Wang, Jindong Li, Shiqi Wang +7
Large language models (LLMs) have not only revolutionized the field of natural language processing (NLP) but also have the potential to bring a paradigm shift in many other fields…
Influence-Oriented Personalized Federated Learning
Yue Tan, Guodong Long, Jing Jiang +1
Federated learning (FL) is a machine learning paradigm where clients with different behaviors and preferences can learn collaboratively without compromising data privacy. Typical F…
Personalized Interpretation on Federated Learning: A Virtual Concepts approach
Peng Yan, Guodong Long, Jing Jiang +1
Tackling non-IID data is an open challenge in federated learning research. Existing FL methods, including robust FL and personalized FL, are designed to improve model performance w…
Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Models
Shengchao Chen, Guodong Long, Jing Jiang +1
This paper demonstrates that pre-trained language models (PLMs) are strong foundation models for on-device meteorological variables modeling. We present LM-Weather, a generic appro…
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…
Client-supervised Federated Learning: Towards One-model-for-all Personalization
Peng Yan, Guodong Long
Personalized Federated Learning (PerFL) is a new machine learning paradigm that delivers personalized models for diverse clients under federated learning settings. Most PerFL metho…