Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
ELSA: Efficient LLM-Centric Split Aggregation for Privacy-Aware Hierarchical Federated Learning over the Network Edge
Xiaohong Yang, Tong Xie, Minghui Liwang +5
Training large language models (LLMs) at the network edge faces fundamental challenges arising from device resource constraints, severe data heterogeneity, and heightened privacy r…
cs.LG2025
You Are Your Own Best Teacher: Achieving Centralized-level Performance in Federated Learning under Heterogeneous and Long-tailed Data
Shanshan Yan, Zexi Li, Chao Wu +4
Data heterogeneity, stemming from local non-IID data and global long-tailed distributions, is a major challenge in federated learning (FL), leading to significant performance gaps…
cs.LG2025
CAPT: Class-Aware Prompt Tuning for Federated Long-Tailed Learning with Vision-Language Model
Shihao Hou, Xinyi Shang, Shreyank N Gowda +4
Effectively handling the co-occurrence of non-IID data and long-tailed distributions remains a critical challenge in federated learning. While fine-tuning vision-language models (V…