Preventing Rank Collapse in Federated Low-Rank Adaptation with Client Heterogeneity

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wu, Fei, Hu, Jia, Min, Geyong, Wang, Shiqiang
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Adaptive Rank Allocation for Federated Parameter-Efficient Fine-Tuning of Language Models
by: Wu, Fei, et al.
Published: (2025)

Towards Robust and Efficient Federated Low-Rank Adaptation with Heterogeneous Clients
by: Koo, Jabin, et al.
Published: (2024)

Towards Federated Low-Rank Adaptation of Language Models with Rank Heterogeneity
by: Byun, Yuji, et al.
Published: (2024)

Incentivizing Multi-Tenant Split Federated Learning for Foundation Models at the Network Edge
by: Li, Songyuan, et al.
Published: (2025)

Federated Continual Learning for Edge-AI: A Comprehensive Survey
by: Wang, Zi, et al.
Published: (2024)

Beyond Aggregation: Guiding Clients in Heterogeneous Federated Learning
by: Wang, Zijian, et al.
Published: (2025)

Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition
by: Liu, Ruyue, et al.
Published: (2024)

FedClust: Tackling Data Heterogeneity in Federated Learning through Weight-Driven Client Clustering
by: Islam, Md Sirajul, et al.
Published: (2024)

FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations
by: Wang, Ziyao, et al.
Published: (2024)

Training Heterogeneous Client Models using Knowledge Distillation in Serverless Federated Learning
by: Chadha, Mohak, et al.
Published: (2024)

FedRA: A Random Allocation Strategy for Federated Tuning to Unleash the Power of Heterogeneous Clients
by: Su, Shangchao, et al.
Published: (2023)

Low-Rank GEMM: Efficient Matrix Multiplication via Low-Rank Approximation with FP8 Acceleration
by: Metere, Alfredo
Published: (2025)

FilFL: Client Filtering for Optimized Client Participation in Federated Learning
by: Fourati, Fares, et al.
Published: (2023)

Tackling Selfish Clients in Federated Learning
by: Augello, Andrea, et al.
Published: (2024)

Efficient Client Selection in Federated Learning
by: Marfo, William, et al.
Published: (2025)

TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning
by: Hu, Gangqiang, et al.
Published: (2024)

AB-Training: A Communication-Efficient Approach for Distributed Low-Rank Learning
by: Coquelin, Daniel, et al.
Published: (2024)

Ranking-based Client Selection with Imitation Learning for Efficient Federated Learning
by: Tian, Chunlin, et al.
Published: (2024)

Optimizing Federated Learning by Entropy-Based Client Selection
by: Lutz, Andreas, et al.
Published: (2024)

Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learning
by: Gao, Zhidong, et al.
Published: (2024)

Gradient Correction in Federated Learning with Adaptive Optimization
by: Chen, Evan, et al.
Published: (2025)

RobustFSM: Submodular Maximization in Federated Setting with Malicious Clients
by: Tran, Duc A., et al.
Published: (2025)

Mitigating Persistent Client Dropout in Asynchronous Decentralized Federated Learning
by: Stępka, Ignacy, et al.
Published: (2025)

FedImpro: Measuring and Improving Client Update in Federated Learning
by: Tang, Zhenheng, et al.
Published: (2024)

FedUV: Uniformity and Variance for Heterogeneous Federated Learning
by: Son, Ha Min, et al.
Published: (2024)

Co-LoRA: Collaborative Model Personalization on Heterogeneous Multi-Modal Clients
by: Seo, Minhyuk, et al.
Published: (2025)

GC-Fed: Gradient Centralized Federated Learning with Partial Client Participation
by: Seo, Jungwon, et al.
Published: (2025)

FedLPS: Heterogeneous Federated Learning for Multiple Tasks with Local Parameter Sharing
by: Jia, Yongzhe, et al.
Published: (2024)

Data-Free Client Contribution Estimation via Logit Maximization for Federated Learning
by: Ukaye, Asim, et al.
Published: (2026)

PNCS:Power-Norm Cosine Similarity for Diverse Client Selection in Federated Learning
by: Li, Liangyan, et al.
Published: (2025)

Compare Where It Matters: Using Layer-Wise Regularization To Improve Federated Learning on Heterogeneous Data
by: Son, Ha Min, et al.
Published: (2021)

Selective Knowledge Sharing for Personalized Federated Learning Under Capacity Heterogeneity
by: Wang, Zheng, et al.
Published: (2024)

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout
by: Liu, Ji, et al.
Published: (2025)

FADAS: Towards Federated Adaptive Asynchronous Optimization
by: Wang, Yujia, et al.
Published: (2024)

FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data
by: Jimenez-Gutierrez, Daniel M., et al.
Published: (2026)

FedFiTS: Fitness-Selected, Slotted Client Scheduling for Trustworthy Federated Learning in Healthcare AI
by: Kahenga, Ferdinand, et al.
Published: (2025)

Federated Learning with Workload Reduction through Partial Training of Client Models and Entropy-Based Data Selection
by: Shi, Hongrui, et al.
Published: (2024)

The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning
by: Li, Shiwei, et al.
Published: (2025)

Heterogeneous Federated Fine-Tuning with Parallel One-Rank Adaptation
by: Zhang, Zikai, et al.
Published: (2026)

HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and Benchmark
by: Zhang, Jianqing, et al.
Published: (2025)