FedLore introduces a communication- and memory-efficient federated learning framework that shares a low-rank optimization basis across clients each round, mitigating subspace fragmentation and enabling exact low-rank aggregation. By refreshing this shared basis across rounds, FedLore allows model updates to exceed the per-round rank budget while maintaining a provable $O(T^{-1/2})$ stationarity bound under standard assumptions. Experiments on vision and language tasks, including federated pre‑training, demonstrate that FedLore outperforms low‑rank adapter baselines and matches or surpasses full‑parameter training while reducing communication and optimizer‑state memory.
By Junkang Liu
arXiv:2601. 16991v3 Announce Type: replace-cross Abstract: Adapting large pre-trained language models to downstream tasks often entails fine-tuning millions of parameters or deploying costly dense weight updates, which hinders their use in resource-constrained environments.
By Longteng Zhang, Sen Wu, Shuai Hou, Zhengyu Qing, Zhuo Zheng, Danning Ke, Qihong Lin, Qiang Wang, Shaohuai Shi, Xiaowen Chu
arXiv:2508. 02932v2 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) has gained popularity as a fine-tuning approach for Large Language Models (LLMs) due to its low resource requirements and good performance.
By Minghao Yan, Zhuang Wang, Zhen Jia, Shivaram Venkataraman, Yida Wang
arXiv:2607. 17913v1 Announce Type: cross Abstract: Distributed Fine-Tuning (DFT) of large-scale Foundation Models (FMs) on resource-constrained edge devices is limited by local compute constraints and communication overhead.
By Bas Meuwissen, Vasileios Tsouvalas, Nirvana Meratnia
The paper introduces Importance-Aware Class-Balanced Sparsification (ICS) for wireless split learning, a lightweight method that ranks feature channels on the server using Grad‑CAM scores from the true‑class logit. By aggregating per‑class scores into a class‑balanced importance vector, clients can retain the top‑N feature channels without extra forward or backward passes, reducing communication overhead while mitigating head‑class bias under label skew. The authors provide a non‑asymptotic convergence bound, analyze overhead against baselines, and extend the approach to parallel split learning and transformer models, demonstrating consistent performance gains, especially under severe non‑i.i.d. data partitions.
By Bumjun Kim, Yoon Huh, Wan Choi
arXiv:2607. 11940v1 Announce Type: cross Abstract: As the scale of large pre-trained models continues to grow, fine-tuning them under limited memory budgets has become increasingly challenging.
By Gengyu Zhang, Haiyin Ran, Zhengbao He, Yuhang Liu, Hanling Tian, Zhehao Huang, Xiaolin Huang
arXiv:2508. 15706v3 Announce Type: replace Abstract: Communication-efficient distributed training algorithms (e.
By Amir Sarfi, Benjamin Th\'erien, Joel Lidin, Eugene Belilovsky
arXiv:2606. 07098v1 Announce Type: cross Abstract: We present SigmaScale, a method for learning auxiliary scaling matrices $S$ to aid truncated Singular Value Decomposition (SVD) based Large Language Model (LLM) compression.
By Ernests Lavrinovics, Marco Letizia, Roy Janco, Shai Segal, Johannes Bjerva, Maurizio Pierini
arXiv:2606. 15963v1 Announce Type: cross Abstract: Federated fine-tuning of large language models using parameter-efficient methods such as LoRA enables privacy-preserving adaptation of foundation models.
By Muhammad Waseem, Nurbek Tastan, Andrej Jovanovic, Nicholas D. Lane, Nils Lukas, Karthik Nandakumar, Samuel Horvath
arXiv:2606. 19528v1 Announce Type: cross Abstract: Fine-tuning of Large Language Models (LLMs) using Low-Rank Adaptation (LoRA) on an end-user's data offers personalized experiences while keeping data private, but faces severe memory constraints on consumer hardware.
By Hassan Dbouk, Matthias Reisser, Prathamesh Mandke, Likhita Arun Navali, Christos Louizos
The paper introduces Low‑Rank Conditional Computation (LRCC), a method that adds token‑dependent computation to pretrained language models by training lightweight routers to select among nested low‑rank paths while keeping low‑rank factors frozen. LRCC is evaluated on Llama and Qwen models, showing improved predictive performance over static low‑rank compression, including a 7.6‑percentage‑point gain in downstream accuracy on Llama‑2‑7B. The study also demonstrates that LRCC enhances perplexity and downstream accuracy on Llama‑3.2‑1B at matched decoding latency and analyzes the routers’ path choices to assess token‑wise path usefulness.
By Thomas Vaitses Fontanari, Maximo Eduardo Rulli, Federico Alvetreti, Donatella Genovese, Simone Scardapane
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