arXiv:2511. 19959v2 Announce Type: replace Abstract: Federated learning (FL) has been extensively studied as a privacy-preserving training paradigm.
By Yujia Wang, Yuanpu Cao, Jinghui Chen
FlexP-SFT introduces an aggregation-free framework for personalized split federated fine-tuning of large language models, eliminating the client-side aggregation step that traditionally causes communication bottlenecks and straggler issues. The method employs a layer‑flexible alignment strategy to balance personalization and generalization without global synchronization, and formulates split‑ratio selection as a resource‑aware discrete optimization problem. Experiments demonstrate that FlexP-SFT improves both accuracy and latency compared to baselines, achieving a superior resource‑accuracy trade‑off.
By Jiaxiang Geng, Tianjun Yuan, Pengchao Han, Ying Gao, Xianhao Chen, Bing Luo
arXiv:2609.39074v1 Announce Type: cross
Abstract: Federated learning (FL) on memory-constrained edge devices faces a dilemma: first-order (FO) optimization (i.e., backpropagation) demands substantial...
By Qiyuan Chen, Xian Wu, Yanan Ma, Xianhao Chen
arXiv:2606. 03209v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) in privacy-sensitive and resource-constrained environments remains challenging.
By Yunsheng Yuan, Shaowei Li, Kai Wang, Zhongyuan Sun, Zheng Zhang, Kai Han, Jun Luo, Feng Li
arXiv:2608. 07974v1 Announce Type: new Abstract: Large language model (LLM) fine-tuning at the edge adapts the model to scenario-specific data while preserving privacy.
By Wentao Dai, Xuanran Li, Yuxiang Zhang, Ming Tang, Chao Huang
arXiv:2601. 04710v2 Announce Type: replace-cross Abstract: Fine-tuning large language models (LLMs) achieves strong performance but is often limited by the memory overhead of backpropagation.
By Feihu Jin, Shipeng Cen, Ying Tan