41 citations · 229 across the 71 of their papers we have counts for
4 papers · 1 filter
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…
SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework
Yuxin Zhang, Zheng Lin, Zhe Chen +5
Traditional federated learning (FL) frameworks rely heavily on terrestrial networks, where coverage limitations and increasing bandwidth congestion significantly hinder model conve…
Spectrum-aware Multi-hop Task Routing in Vehicle-assisted Collaborative Edge Computing
Yiqin Deng, Haixia Zhang, Xianhao Chen +1
Multi-access edge computing (MEC) is a promising technology to enhance the quality of service, particularly for low-latency services, by enabling computing offloading to edge serve…