2 papers
cs.IT2026
FedLoDrop: Federated LoRA with Dropout for Generalized LLM Fine-tuning
Sijing Xie, Dingzhu Wen, Changsheng You +3
Fine-tuning (FT) large language models (LLMs) is crucial for adapting general-purpose models to specific tasks, enhancing accuracy and relevance with minimal resources. To further…
cs.DC2025
Communication-and-Computation Efficient Split Federated Learning: Gradient Aggregation and Resource Management
Yipeng Liang, Qimei Chen, Guangxu Zhu +2
With the prevalence of Large Learning Models (LLM), Split Federated Learning (SFL), which divides a learning model into server-side and client-side models, has emerged as an appeal…