FedRand: Enhancing Privacy in Federated Learning with Randomized LoRA Subparameter Updates
Fuente:
arXiv
Saved in:
| Main Authors: | Park, Sangwoo, Lee, Seanie, Kim, Byungjoo, Hwang, Sung Ju |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
FedSVD: Adaptive Orthogonalization for Private Federated Learning with LoRA
by: Lee, Seanie, et al.
Published: (2025)
by: Lee, Seanie, et al.
Published: (2025)
Adaptive Selection of LoRA Components in Privacy-Preserving Federated Learning
by: Kim, Myoungjun, et al.
Published: (2026)
by: Kim, Myoungjun, et al.
Published: (2026)
FedRot-LoRA: Mitigating Rotational Misalignment in Federated LoRA
by: Zhang, Haoran, et al.
Published: (2026)
by: Zhang, Haoran, et al.
Published: (2026)
pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning
by: Yi, Liping, et al.
Published: (2023)
by: Yi, Liping, et al.
Published: (2023)
Improving LoRA in Privacy-preserving Federated Learning
by: Sun, Youbang, et al.
Published: (2024)
by: Sun, Youbang, et al.
Published: (2024)
FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA
by: Jhunjhunwala, Divyansh, et al.
Published: (2025)
by: Jhunjhunwala, Divyansh, et al.
Published: (2025)
FedTreeLoRA: Reconciling Statistical and Functional Heterogeneity in Federated LoRA Fine-Tuning
by: Bian, Jieming, et al.
Published: (2026)
by: Bian, Jieming, et al.
Published: (2026)
Rethinking LoRA for Privacy-Preserving Federated Learning in Large Models
by: Liu, Jin, et al.
Published: (2026)
by: Liu, Jin, et al.
Published: (2026)
Two Stage Wireless Federated LoRA Fine-Tuning with Sparsified Orthogonal Updates
by: Kim, Bumjun, et al.
Published: (2025)
by: Kim, Bumjun, et al.
Published: (2025)
FedLoDrop: Federated LoRA with Dropout for Generalized LLM Fine-tuning
by: Xie, Sijing, et al.
Published: (2025)
by: Xie, Sijing, et al.
Published: (2025)
Riemannian Optimization for LoRA on the Stiefel Manifold
by: Park, Juneyoung, et al.
Published: (2025)
by: Park, Juneyoung, et al.
Published: (2025)
FedLoRA-Optimizer: Federated LoRA Fine-Tuning with Global and Local Optimization in Heterogeneous Data Scenarios
by: Zhao, Jianzhe, et al.
Published: (2025)
by: Zhao, Jianzhe, et al.
Published: (2025)
LoRA and Privacy: When Random Projections Help (and When They Don't)
by: Hu, Yaxi, et al.
Published: (2026)
by: Hu, Yaxi, et al.
Published: (2026)
R-LoRA: Randomized Multi-Head LoRA for Efficient Multi-Task Learning
by: Liu, Jinda, et al.
Published: (2025)
by: Liu, Jinda, et al.
Published: (2025)
FedMomentum: Preserving LoRA Training Momentum in Federated Fine-Tuning
by: Yan, Peishen, et al.
Published: (2026)
by: Yan, Peishen, et al.
Published: (2026)
Drug Discovery with Dynamic Goal-aware Fragments
by: Lee, Seul, et al.
Published: (2023)
by: Lee, Seul, et al.
Published: (2023)
Promoting Data and Model Privacy in Federated Learning through Quantized LoRA
by: Zhu, JianHao, et al.
Published: (2024)
by: Zhu, JianHao, et al.
Published: (2024)
WinFLoRA: Incentivizing Client-Adaptive Aggregation in Federated LoRA under Privacy Heterogeneity
by: Kou, Mengsha, et al.
Published: (2026)
by: Kou, Mengsha, et al.
Published: (2026)
LoRA Provides Differential Privacy by Design via Random Sketching
by: Malekmohammadi, Saber, et al.
Published: (2024)
by: Malekmohammadi, Saber, et al.
Published: (2024)
LoRA-FAIR: Federated LoRA Fine-Tuning with Aggregation and Initialization Refinement
by: Bian, Jieming, et al.
Published: (2024)
by: Bian, Jieming, et al.
Published: (2024)
DiffusionNAG: Predictor-guided Neural Architecture Generation with Diffusion Models
by: An, Sohyun, et al.
Published: (2023)
by: An, Sohyun, et al.
Published: (2023)
Run LoRA Run: Faster and Lighter LoRA Implementations
by: Cherniuk, Daria, et al.
Published: (2023)
by: Cherniuk, Daria, et al.
Published: (2023)
Set-based Meta-Interpolation for Few-Task Meta-Learning
by: Lee, Seanie, et al.
Published: (2022)
by: Lee, Seanie, et al.
Published: (2022)
Robust Federated Finetuning of LLMs via Alternating Optimization of LoRA
by: Chen, Shuangyi, et al.
Published: (2025)
by: Chen, Shuangyi, et al.
Published: (2025)
Federated LoRA with Sparse Communication
by: Kuo, Kevin, et al.
Published: (2024)
by: Kuo, Kevin, et al.
Published: (2024)
SDFLoRA: Selective Decoupled Federated LoRA for Privacy-preserving Fine-tuning with Heterogeneous Clients
by: Shen, Zhikang, et al.
Published: (2026)
by: Shen, Zhikang, et al.
Published: (2026)
LoRA on the Go: Instance-level Dynamic LoRA Selection and Merging
by: Lee, Seungeon, et al.
Published: (2025)
by: Lee, Seungeon, et al.
Published: (2025)
Cost-Sensitive Multi-Fidelity Bayesian Optimization with Transfer of Learning Curve Extrapolation
by: Lee, Dong Bok, et al.
Published: (2024)
by: Lee, Dong Bok, et al.
Published: (2024)
S-LoRA: Serving Thousands of Concurrent LoRA Adapters
by: Sheng, Ying, et al.
Published: (2023)
by: Sheng, Ying, et al.
Published: (2023)
Fed-pilot: Optimizing LoRA Allocation for Efficient Federated Fine-Tuning with Heterogeneous Clients
by: Zhang, Zikai, et al.
Published: (2024)
by: Zhang, Zikai, et al.
Published: (2024)
CE-LoRA: Computation-Efficient LoRA Fine-Tuning for Language Models
by: Chen, Guanduo, et al.
Published: (2025)
by: Chen, Guanduo, et al.
Published: (2025)
LoRA Diffusion: Zero-Shot LoRA Synthesis for Diffusion Model Personalization
by: Smith, Ethan, et al.
Published: (2024)
by: Smith, Ethan, et al.
Published: (2024)
Self-Supervised Dataset Distillation for Transfer Learning
by: Lee, Dong Bok, et al.
Published: (2023)
by: Lee, Dong Bok, et al.
Published: (2023)
Rethinking the Rank Threshold for LoRA Fine-Tuning
by: Park, Juneyoung
Published: (2026)
by: Park, Juneyoung
Published: (2026)
It Takes Two: Complementary Self-Distillation for Contextual Integrity in LLMs
by: Park, Sangwoo, et al.
Published: (2026)
by: Park, Sangwoo, et al.
Published: (2026)
Robust Federated Finetuning of Foundation Models via Alternating Minimization of LoRA
by: Chen, Shuangyi, et al.
Published: (2024)
by: Chen, Shuangyi, et al.
Published: (2024)
Mitigating Unintended Memorization with LoRA in Federated Learning for LLMs
by: Bossy, Thierry, et al.
Published: (2025)
by: Bossy, Thierry, et al.
Published: (2025)
A Note on LoRA
by: Fomenko, Vlad, et al.
Published: (2024)
by: Fomenko, Vlad, et al.
Published: (2024)
Block-Diagonal LoRA for Eliminating Communication Overhead in Tensor Parallel LoRA Serving
by: Wang, Xinyu, et al.
Published: (2025)
by: Wang, Xinyu, et al.
Published: (2025)
Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation
by: Zhang, Zikai, et al.
Published: (2025)
by: Zhang, Zikai, et al.
Published: (2025)
Similar Items
-
FedSVD: Adaptive Orthogonalization for Private Federated Learning with LoRA
by: Lee, Seanie, et al.
Published: (2025) -
Adaptive Selection of LoRA Components in Privacy-Preserving Federated Learning
by: Kim, Myoungjun, et al.
Published: (2026) -
FedRot-LoRA: Mitigating Rotational Misalignment in Federated LoRA
by: Zhang, Haoran, et al.
Published: (2026) -
pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning
by: Yi, Liping, et al.
Published: (2023) -
Improving LoRA in Privacy-preserving Federated Learning
by: Sun, Youbang, et al.
Published: (2024)