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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.DC2024★ 2 cited
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…