arXiv AI
Sep 15

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

FLoKD is an adaptive knowledge‑distillation framework designed for federated fine‑tuning of low‑rank LLMs over wireless networks. It transmits intermediate LoRA activations instead of full parameters or token‑level logits, and uses transformer block importance scoring plus dataset selection to reduce communication. Experiments on WikiText‑103, PTB, and Dialog show a 50‑65% reduction in communication while maintaining competitive perplexity.

By Xinlu Zhang, Na Yan, Yang Su, Yansha Deng, Toktam Mahmoodi
arXiv AI
Sep 15

LayerRoute: Adaptive Layer-Skipping with LoRA-Preserved Quality for Efficient LLM Inference

LayerRoute is a parameter‑efficient technique that enables adaptive skipping of transformer layers in large language models. It adds a lightweight per‑layer router (~21.5K parameters) and LoRA adapters (rank 8, ~1.08M parameters) to each of the 24 blocks in Qwen2.5‑0.5B‑Instruct, training them jointly with a gate‑regularized language‑modeling objective. Across ten independent runs, the method consistently identifies nine middle layers as skip‑eligible, achieves a verified wall‑clock speedup of 1.02x–1.06x, and improves perplexity by an average of 1.16 points, while the router’s decisions vary per input, confirming genuine adaptive behavior.

By Prateek Kumar Sikdar
arXiv AI
Jul 22

Federated Lightweight Fine-Tuning

arXiv:2607. 18343v1 Announce Type: cross Abstract: Federated fine-tuning is bottlenecked by communication: FedAvg and pseudo-gradient schemes transmit a payload that scales with the model, and gradient compression shrinks it by only a constant factor.

By Radhakrishna Achanta, Will Reed