SplitFT: An Adaptive Federated Split Learning System For LLMs Fine-Tuning
Federated Split Learning has been identified as an efficient approach to address the computational resource constraints of clients in classical federated learning, while guaranteeing data privacy for distributed model training across data owners. However, it faces some critical challenges when such a training strategy meets large language models (LLMs) for fine-tuning. Such challenges include setting the cutlayer adaptively across different clients to address the data and device heterogeneity issues, which affect the system performance significantly. In addition, efficiently reducing the communication overhead during the fine-tuning procedure is also another challenge. No work tries to ad...
| Authors | Yimeng Shan, Zhaorui Zhang, Sheng Di et al. |
|---|---|
| Primary category | cs.DC |
| Published | 29 April 2026 |
| Last updated at source | 29 April 2026 |
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