FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices

Fuente: arXiv

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Bibliographic Details
Main Authors:	Li, Changyu, Huang, Shuanghong, Liu, Jiashen, Lei, Ming, Xing, Jidu, Wu, Kaishun, Wang, Lu, Luo, Fei
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence C.2.4; I.2.7
Online Access:	Acceder al recurso
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Internet

https://arxiv.org/abs/2604.25421