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FLoKD: Adaptive Knowledge Distillation for Federated Low-Rank LLM over Wireless Networks

arXiv · AI, language, vision and robotics · article · Sep 11, 2026 · UTC

Large language models (LLMs) have demonstrated strong capabilities across a wide range of natural language processing tasks. However, conventional fine-tuning typically relies on centralized data collection, bringing in privacy concerns. Federated learning (FL) enables collaborative LLM fine-tuning without sharing raw client data, but its deployment over bandwidth-constrained wireless networks is hindered by the communication overhead of model-parameter transmission. Although Low-Rank Adaptation (LoRA) reduces the number of trainable parameters, its communication cost still increases with mode

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