3 papers
cs.CL2025
Enhancing Large Language Models for Mobility Analytics with Semantic Location Tokenization
Yile Chen, Yicheng Tao, Yue Jiang +3
The widespread adoption of location-based services has led to the generation of vast amounts of mobility data, providing significant opportunities to model user movement dynamics w…
cs.CL2025
Efficient Shapley Value-based Non-Uniform Pruning of Large Language Models
Chuan Sun, Han Yu, Lizhen Cui +1
Pruning large language models (LLMs) is a promising solution for reducing model sizes and computational complexity while preserving performance. Traditional layer-wise pruning meth…
cs.LG2025
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…