1 citations · 1 across the 4 of their papers we have counts for
4 papers
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…
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…
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.…
Adaptive Federated Learning in Heterogeneous Wireless Networks with Independent Sampling
Jiaxiang Geng, Yanzhao Hou, Xiaofeng Tao +2
Federated Learning (FL) algorithms commonly sample a random subset of clients to address the straggler issue and improve communication efficiency. While recent works have proposed…