SeedFlood is a novel decentralized fine‑tuning method for large language models that scales to billions of parameters and hundreds of clients. It leverages the seed‑reconstructible structure of zeroth‑order gradients to reduce message sizes to near‑zero, enabling efficient flooding across the network. Experiments show SeedFlood outperforms standard zeroth‑order baselines in communication efficiency and generalization, and rivals first‑order gossip methods while incurring far less communication cost.
By Jihun Kim, Dongyeop Lee, Namhoon Lee
arXiv:2606. 11081v1 Announce Type: cross Abstract: Communication-efficient pre-training of LLMs is increasingly important as training draws on compute distributed across clusters, data centers, and lower-bandwidth links.
By Pietro Cagnasso, Eugene Belilovsky, Edouard Oyallon
Communication-efficient pre-training of LLMs is increasingly important as training draws on compute distributed across clusters, data centers, and lower-bandwidth links. Many practical methods reduce communication frequency but still rely on synchronous All-Reduce operations that maintain identical model states and tie progress to global collectives.
arXiv:2606. 06154v1 Announce Type: new Abstract: Federated fine-tuning of foundation models using Low-Rank Adaptation (LoRA) offers a communication efficient solution for distributed learning.
By Sunny Gupta, Shambhavi Shanker, Amit Sethi
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:2609.13512v1 Announce Type: cross
Abstract: Federated fine-tuning of large language models with low-rank adaptation reduces per-client trainable parameters, but client-to-server communication r...
By Jerry Adams Franklin
FedSubMuon introduces a communication‑efficient federated fine‑tuning approach for large language models by optimizing compact coefficient matrices within shared structured subspaces, thereby keeping Muon’s matrix‑aware optimization while reducing client upload size. An accuracy‑oriented variant, FedSubMuon‑GT, further adapts subspace bases using projected gradients to better align with task‑relevant directions. Experiments on instruction tuning and mathematical reasoning demonstrate that FedSubMuon‑GT achieves the best overall accuracy on most dataset‑model pairs, while FedSubMuon outperforms all matched‑budget baselines and reduces communication by up to 5.5× on Llama‑1B and 1.4× on Qwen‑4B compared to the closest baseline.
By Shaolong Chen, Youming Tao, Shuzhen Chen, Falko Dressler, Qingqing Ye, Di Wang
arXiv:2607. 20914v1 Announce Type: new Abstract: Federated parameter-efficient fine-tuning (PEFT) enables communication-efficient adaptation of large pretrained models on decentralized edge data, but it remains fragile under non-IID client heterogeneity.
By Shiva Raj Pokhrel, Dipsan Bhattarai, Anwar Walid
arXiv:2608. 15381v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) enables efficient federated fine-tuning of large language models, but its factorized parameterization creates a tension between accurate aggregation of local updates and continuity of locally optimized factors.
By Juseok Jeon, Ramy E. Ali, Doyun Kwon, Myungbeom Her, Jinhwi Kim, Jinhyun So
arXiv:2607. 21074v1 Announce Type: new Abstract: Fine-tuning Vision Transformers (ViTs) with low-rank adapters (LoRA) promises better communication efficiency under federated setup, yet existing aggregation strategies face fundamental limitations.
By Hariharan Ramesh, Jyotikrishna Dass
arXiv:2608. 15256v1 Announce Type: new Abstract: Collaborative training in distributed semantic communication (DSC) networks typically relies on decentralized federated learning (DFL).
By Lin Yin, Tiejun Lv, Weicai Li, Xi Yu, Xiaoyu He
Low-Rank Adaptation (LoRA) enables efficient federated fine-tuning of large language models, but its factorized parameterization creates a tension between accurate aggregation of local updates and con...