3 papers
cs.DC2025
Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices
Xiaopei Chen, Liang Li, Fei Ji +1
In this paper, we propose an edge-assisted split federated learning framework to facilitate large language model (LLM) fine-tuning on heterogeneous mobile devices while alleviating…
cs.LG2025
MobiLLM: Enabling LLM Fine-Tuning on the Mobile Device via Server Assisted Side Tuning
Liang Li, Xingke Yang, Wen Wu +5
Large Language Model (LLM) at mobile devices and its potential applications never fail to fascinate. However, on-device LLM fine-tuning poses great challenges due to extremely high…
cs.DC2025
RingAda: Pipelining Large Model Fine-Tuning on Edge Devices with Scheduled Layer Unfreezing
Liang Li, Xiaopei Chen, Wen Wu
To enable large model (LM) based edge intelligent service provisioning, on-device fine-tuning with locally personalized data allows for continuous and privacy-preserving LM customi…