Communication-Efficient Federated Low-Rank Update Algorithm and its Connection to Implicit Regularization
Fuente:
arXiv
Saved in:
| Main Authors: | Park, Haemin, Klabjan, Diego |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Regret Bounds and Reinforcement Learning Exploration of EXP-based Algorithms
by: Xu, Mengfan, et al.
Published: (2020)
by: Xu, Mengfan, et al.
Published: (2020)
Is Prompt Selection Necessary for Task-Free Online Continual Learning?
by: Park, Seoyoung, et al.
Published: (2026)
by: Park, Seoyoung, et al.
Published: (2026)
Technical Debt in In-Context Learning: Diminishing Efficiency in Long Context
by: Joo, Taejong, et al.
Published: (2025)
by: Joo, Taejong, et al.
Published: (2025)
Automatic Piecewise Linear Regression for Predicting Student Learning Satisfaction
by: Choi, Haemin, et al.
Published: (2025)
by: Choi, Haemin, et al.
Published: (2025)
Adaptive Regularization of Representation Rank as an Implicit Constraint of Bellman Equation
by: He, Qiang, et al.
Published: (2024)
by: He, Qiang, et al.
Published: (2024)
Multi-Layer Attention-Based Explainability via Transformers for Tabular Data
by: Gavito, Andrea Treviño, et al.
Published: (2023)
by: Gavito, Andrea Treviño, et al.
Published: (2023)
Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition
by: Liu, Ruyue, et al.
Published: (2024)
by: Liu, Ruyue, et al.
Published: (2024)
FedSAUC: A Similarity-Aware Update Control for Communication-Efficient Federated Learning in Edge Computing
by: Lee, Ming-Lun, et al.
Published: (2025)
by: Lee, Ming-Lun, et al.
Published: (2025)
Aggregating Low Rank Adapters in Federated Fine-tuning
by: Trautmann, Evelyn, et al.
Published: (2025)
by: Trautmann, Evelyn, et al.
Published: (2025)
Global Low-Rank, Local Full-Rank: The Holographic Encoding of Learned Algorithms
by: Xu, Yongzhong
Published: (2026)
by: Xu, Yongzhong
Published: (2026)
GraLoRA: Granular Low-Rank Adaptation for Parameter-Efficient Fine-Tuning
by: Jung, Yeonjoon, et al.
Published: (2025)
by: Jung, Yeonjoon, et al.
Published: (2025)
SBoRA: Low-Rank Adaptation with Regional Weight Updates
by: Po, Lai-Man, et al.
Published: (2024)
by: Po, Lai-Man, et al.
Published: (2024)
Initialization using Update Approximation is a Silver Bullet for Extremely Efficient Low-Rank Fine-Tuning
by: Ponkshe, Kaustubh, et al.
Published: (2024)
by: Ponkshe, Kaustubh, et al.
Published: (2024)
Ravan: Multi-Head Low-Rank Adaptation for Federated Fine-Tuning
by: Raje, Arian, et al.
Published: (2025)
by: Raje, Arian, et al.
Published: (2025)
Variational Deep Learning via Implicit Regularization
by: Wenger, Jonathan, et al.
Published: (2025)
by: Wenger, Jonathan, et al.
Published: (2025)
Q3R: Quadratic Reweighted Rank Regularizer for Effective Low-Rank Training
by: Ghosh, Ipsita, et al.
Published: (2025)
by: Ghosh, Ipsita, et al.
Published: (2025)
LoRDO: Distributed Low-Rank Optimization with Infrequent Communication
by: Jovanović, Andrej, et al.
Published: (2026)
by: Jovanović, Andrej, et al.
Published: (2026)
DRSLF: Double Regularized Second-Order Low-Rank Representation for Web Service QoS Prediction
by: Wu, Hao, et al.
Published: (2025)
by: Wu, Hao, et al.
Published: (2025)
Towards Robust and Efficient Federated Low-Rank Adaptation with Heterogeneous Clients
by: Koo, Jabin, et al.
Published: (2024)
by: Koo, Jabin, et al.
Published: (2024)
Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank Decomposition
by: Wu, Xinghao, et al.
Published: (2024)
by: Wu, Xinghao, et al.
Published: (2024)
FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation
by: Peng, Zihao, et al.
Published: (2025)
by: Peng, Zihao, et al.
Published: (2025)
A Parameter Update Balancing Algorithm for Multi-task Ranking Models in Recommendation Systems
by: Yuan, Jun, et al.
Published: (2024)
by: Yuan, Jun, et al.
Published: (2024)
Memory-Efficient Fine-Tuning via Low-Rank Activation Compression
by: Shi, Jiang-Xin, et al.
Published: (2025)
by: Shi, Jiang-Xin, et al.
Published: (2025)
Joint Tensor-Train Parameterization for Efficient and Expressive Low-Rank Adaptation
by: Qi, Jun, et al.
Published: (2025)
by: Qi, Jun, et al.
Published: (2025)
Enhancing Parameter Efficiency and Generalization in Large-Scale Models: A Regularized and Masked Low-Rank Adaptation Approach
by: Mao, Yuzhu, et al.
Published: (2024)
by: Mao, Yuzhu, et al.
Published: (2024)
Beyond Factor Aggregation: Gauge-Aware Low-Rank Server Representations for Federated LoRA
by: Chen, Jinqian, et al.
Published: (2026)
by: Chen, Jinqian, et al.
Published: (2026)
Connecting Federated ADMM to Bayes
by: Swaroop, Siddharth, et al.
Published: (2025)
by: Swaroop, Siddharth, et al.
Published: (2025)
FLAIN: Mitigating Backdoor Attacks in Federated Learning via Flipping Weight Updates of Low-Activation Input Neurons
by: Ding, Binbin, et al.
Published: (2024)
by: Ding, Binbin, et al.
Published: (2024)
Improved Generalization Bounds for Communication Efficient Federated Learning
by: Gholami, Peyman, et al.
Published: (2024)
by: Gholami, Peyman, et al.
Published: (2024)
Efficient Low Rank Attention for Long-Context Inference in Large Language Models
by: Li, Tenghui, et al.
Published: (2025)
by: Li, Tenghui, et al.
Published: (2025)
Communication-Efficient Federated Learning with Accelerated Client Gradient
by: Kim, Geeho, et al.
Published: (2022)
by: Kim, Geeho, et al.
Published: (2022)
A Primal-Dual Algorithm for Hybrid Federated Learning
by: Overman, Tom, et al.
Published: (2022)
by: Overman, Tom, et al.
Published: (2022)
Provably Mitigating Overoptimization in RLHF: Your SFT Loss is Implicitly an Adversarial Regularizer
by: Liu, Zhihan, et al.
Published: (2024)
by: Liu, Zhihan, et al.
Published: (2024)
Efficient Federated Learning with Timely Update Dissemination
by: Jia, Juncheng, et al.
Published: (2025)
by: Jia, Juncheng, et al.
Published: (2025)
TalkLoRA: Communication-Aware Mixture of Low-Rank Adaptation for Large Language Models
by: Mu, Lin, et al.
Published: (2026)
by: Mu, Lin, et al.
Published: (2026)
Degree of Staleness-Aware Data Updating in Federated Learning
by: Liu, Tao, et al.
Published: (2025)
by: Liu, Tao, et al.
Published: (2025)
Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs
by: Abillama, Pierre, et al.
Published: (2025)
by: Abillama, Pierre, et al.
Published: (2025)
Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models
by: Zhang, Longteng, et al.
Published: (2026)
by: Zhang, Longteng, et al.
Published: (2026)
MODE: Efficient Time Series Prediction with Mamba Enhanced by Low-Rank Neural ODEs
by: Chen, Xingsheng, et al.
Published: (2026)
by: Chen, Xingsheng, et al.
Published: (2026)
CoLA: Compute-Efficient Pre-Training of LLMs via Low-Rank Activation
by: Liu, Ziyue, et al.
Published: (2025)
by: Liu, Ziyue, et al.
Published: (2025)
Similar Items
-
Regret Bounds and Reinforcement Learning Exploration of EXP-based Algorithms
by: Xu, Mengfan, et al.
Published: (2020) -
Is Prompt Selection Necessary for Task-Free Online Continual Learning?
by: Park, Seoyoung, et al.
Published: (2026) -
Technical Debt in In-Context Learning: Diminishing Efficiency in Long Context
by: Joo, Taejong, et al.
Published: (2025) -
Automatic Piecewise Linear Regression for Predicting Student Learning Satisfaction
by: Choi, Haemin, et al.
Published: (2025) -
Adaptive Regularization of Representation Rank as an Implicit Constraint of Bellman Equation
by: He, Qiang, et al.
Published: (2024)