3 papers
cs.DC2026
Just Talk Once: Communication-Efficient Split Federated LLM Fine-Tuning on Edge Devices
Jiaxiang Geng, Xianhao Chen, Bing Luo
Large language model (LLM) fine-tuning is increasingly shifting toward data generated on edge devices, where memory, computation, bandwidth, and connectivity constraints make conve…
cs.DC2025
FlexP-SFT: A Flexible Aggregation-Free Framework for On-Device Personalized Split Federated Fine-Tuning of LLMs
Jiaxiang Geng, Tianjun Yuan, Pengchao Han +3
To fine-tune large language models (LLMs) over private data, federated learning (FL) has emerged as a promising paradigm. However, the prohibitive memory and communication demands…
cs.LG2025
Adaptive Federated LoRA in Heterogeneous Wireless Networks with Independent Sampling
Yanzhao Hou, Jiaxiang Geng, Boyu Li +4
Federated LoRA has emerged as a promising technique for efficiently fine-tuning large language models (LLMs) on distributed devices by reducing the number of trainable parameters.…