7 papers
MobileFineTuner: A Mobile-Native Framework for On-Device LLM Fine-Tuning in Real-World Embedded AI Applications
Jiaxiang Geng, Lunyu Zhao, Yiyi Lu +1
Large language models (LLMs) are moving from cloud-centric services toward on-device embedded AI, where models interact with private, longitudinal signals sensed from users and the…
EdgeFlowerTune: Evaluating Federated LLM Fine-Tuning Under Realistic Edge System Constraints
Jiaxiang Geng, Yiyi Lu, Lunyu Zhao +3
Federated fine-tuning offers a promising paradigm for adapting large language models (LLMs) on edge devices by leveraging the rich, diverse, and continuously generated data from sm…
Flexible Personalized Split Federated Learning for On-Device Fine-Tuning of Foundation Models
Tianjun Yuan, Jiaxiang Geng, Pengchao Han +2
Fine-tuning foundation models is critical for superior performance on personalized downstream tasks, compared to using pre-trained models. Collaborative learning can leverage local…
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.…
FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selection
Jiaxiang Geng, Boyu Li, Xiaoqi Qin +4
Training latency is critical for the success of numerous intrigued applications ignited by federated learning (FL) over heterogeneous mobile devices. By revolutionarily overlapping…
WHALE-FL: Wireless and Heterogeneity Aware Latency Efficient Federated Learning over Mobile Devices via Adaptive Subnetwork Scheduling
Huai-an Su, Jiaxiang Geng, Liang Li +5
As a popular distributed learning paradigm, federated learning (FL) over mobile devices fosters numerous applications, while their practical deployment is hindered by participating…