paperAugust 2026Unreviewed

Efficient and Privacy-Preserving Federated Knowledge Learning for Distributed LLM

Wei Sun, Xianda Wang, Zhicheng Liang, Tianyi Gong, Wanshun Lan, Yingchun Chen, Haoyue Li, Fangxin Wang

IEEE Transactions on Mobile Computing

Abstract

Federated learning (FL) stands out as a promising solution to address the ever-increasing data scarcity problem of large language model (LLM) training through collecting data from distributed sources. The unique challenges however exist in three aspects, the extreme large communication overhead due to frequent model aggregation, the suboptimal performance due to data heterogeneity, and privacy leakage risk due to model parameter exchange. To address these issues, we propose Federated Knowledge L

Framework mappings

OWASP Top 10 for LLM Applications

LLM02Sensitive Information Disclosure

MITRE ATLAS

AML.T0024.000Infer Training Data Membership

Suggested from the entry's categories.

Cite

@article{sun2026efficient,
  title = {{Efficient and Privacy-Preserving Federated Knowledge Learning for Distributed LLM}},
  author = {Wei Sun and Xianda Wang and Zhicheng Liang and Tianyi Gong and Wanshun Lan and Yingchun Chen and Haoyue Li and Fangxin Wang},
  year = {2026},
  month = aug,
  journal = {IEEE Transactions on Mobile Computing},
  doi = {10.1109/TMC.2026.3674545},
  url = {https://www.semanticscholar.org/paper/f85d3d9c7b08f2e213a1e5c4d37b9256ead2f4a7}
}