arXiv AI
FedLAFP: Low-Rank Aggregation Meets Full-Rank Personalization in Federated Fine-Tuning
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arXiv AI
Sep 2
Breaking the Structural Identity: Personalized Federated LoRA Fine-tuning under Rank Heterogeneity
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 AI
Jun 16
PreLort: Prefix-Nested LoRA for Federated Fine-Tuning under Rank Heterogeneity
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.
arXiv AI
Jun 6
Amortizing Federated Adaptation: Hypernetwork Driven LoRA for Personalized Foundation Models
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.
arXiv AI
4d ago
CF-LoRA: Decoupled Factor Aggregation and Adaptation-Aware Client Clustering for Federated LoRA Fine-Tuning
arXiv:2609.36986v1 Announce Type: new Abstract: Federated LoRA fine-tuning enables parameter-efficient adaptation of pre-trained models without sharing private data, but suffers from two fundamental...
arXiv AI
Jun 16
SDFLoRA: Selective Decoupled Federated LoRA for Privacy-preserving Fine-tuning with Heterogeneous Clients
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 Machine Learning
Aug 13
Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning
arXiv:2608. 12108v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training across distributed clients while keeping data local.