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.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
The paper introduces FedRoRA, a federated learning framework that combines Low‑Rank Adaptation (LoRA) with rank‑heterogeneous personalization. It separates model adaptation into shared global directions and client‑specific rank‑wise magnitudes, using SVD on the server to extract a global subspace and a personalized projection with top‑k selection for each client. Experiments on natural language understanding and generation tasks show that FedRoRA outperforms existing state‑of‑the‑art methods.
arXiv:2601. 11219v3 Announce Type: replace-cross Abstract: Federated learning (FL) for large language models (LLMs) has attracted increasing attention as a privacy-preserving approach for adapting models over distributed data, where parameter-efficient methods such as Low-Rank Adaptation (LoRA) are widely adopted to reduce communication and memory costs.
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.
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...
arXiv:2609.37033v1 Announce Type: new Abstract: Federated parameter-efficient fine-tuning enables clients to adapt pre-trained models without sharing raw data or communicating the full model, but sta...
arXiv:2602. 23638v3 Announce Type: replace-cross Abstract: Federated LoRA provides a communication-efficient mechanism for fine-tuning large language models on decentralized data.