Copertina

pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Yi, Liping, Yu, Han, Wang, Gang, Liu, Xiaoguang, Li, Xiaoxiao
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Machine Learning Distributed, Parallel, and Cluster Computing
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

SHE-LoRA: Selective Homomorphic Encryption for Federated Tuning with Heterogeneous LoRA
di: Liu, Jianmin, et al.
Pubblicazione: (2025)

Fed-pilot: Optimizing LoRA Allocation for Efficient Federated Fine-Tuning with Heterogeneous Clients
di: Zhang, Zikai, et al.
Pubblicazione: (2024)

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation
di: Zhang, Zikai, et al.
Pubblicazione: (2025)

pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learning
di: Yi, Liping, et al.
Pubblicazione: (2024)

ALTO: Adaptive LoRA Tuning and Orchestration for Heterogeneous LoRA Training Workloads
di: Zuo, Jingwei, et al.
Pubblicazione: (2026)

FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning
di: Yi, Liping, et al.
Pubblicazione: (2023)

FDLoRA: Personalized Federated Learning of Large Language Model via Dual LoRA Tuning
di: QI, Jiaxing, et al.
Pubblicazione: (2024)

Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models
di: Cho, Yae Jee, et al.
Pubblicazione: (2024)

Federated LoRA with Sparse Communication
di: Kuo, Kevin, et al.
Pubblicazione: (2024)

FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA
di: Jhunjhunwala, Divyansh, et al.
Pubblicazione: (2025)

S-LoRA: Serving Thousands of Concurrent LoRA Adapters
di: Sheng, Ying, et al.
Pubblicazione: (2023)

FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning
di: Li, Rukuo, et al.
Pubblicazione: (2025)

Federated Model Heterogeneous Matryoshka Representation Learning
di: Yi, Liping, et al.
Pubblicazione: (2024)

Stabilizing Decentralized Federated Fine-Tuning via Topology-Aware Alternating LoRA
di: Wang, Xiaoyu, et al.
Pubblicazione: (2026)

ServerlessLoRA: Minimizing Latency and Cost in Serverless Inference for LoRA-Based LLMs
di: Sui, Yifan, et al.
Pubblicazione: (2025)

LoRAFusion: Efficient LoRA Fine-Tuning for LLMs
di: Zhu, Zhanda, et al.
Pubblicazione: (2025)

Improving LoRA in Privacy-preserving Federated Learning
di: Sun, Youbang, et al.
Pubblicazione: (2024)

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

InfiniLoRA: Disaggregated Multi-LoRA Serving for Large Language Models
di: Chen, Hongyu, et al.
Pubblicazione: (2026)

Rate-My-LoRA: Efficient and Adaptive Federated Model Tuning for Cardiac MRI Segmentation
di: He, Xiaoxiao, et al.
Pubblicazione: (2025)

Robust Federated Finetuning of Foundation Models via Alternating Minimization of LoRA
di: Chen, Shuangyi, et al.
Pubblicazione: (2024)

AutoRank: MCDA Based Rank Personalization for LoRA-Enabled Distributed Learning
di: Chen, Shuaijun, et al.
Pubblicazione: (2024)

RBLA: Rank-Based-LoRA-Aggregation for Fine-tuning Heterogeneous Models in FLaaS
di: Chen, Shuaijun, et al.
Pubblicazione: (2024)

HAFLQ: Heterogeneous Adaptive Federated LoRA Fine-tuned LLM with Quantization
di: Su, Yang, et al.
Pubblicazione: (2024)

Serving Heterogeneous LoRA Adapters in Distributed LLM Inference Systems
di: Jaiswal, Shashwat, et al.
Pubblicazione: (2025)

EcoLoRA: Communication-Efficient Federated Fine-Tuning of Large Language Models
di: Liu, Han, et al.
Pubblicazione: (2025)

JORA: JAX Tensor-Parallel LoRA Library for Retrieval Augmented Fine-Tuning
di: Tahir, Anique, et al.
Pubblicazione: (2024)

LoRA-C: Parameter-Efficient Fine-Tuning of Robust CNN for IoT Devices
di: Ding, Chuntao, et al.
Pubblicazione: (2024)

Fed-SB: A Silver Bullet for Extreme Communication Efficiency and Performance in (Private) Federated LoRA Fine-Tuning
di: Singhal, Raghav, et al.
Pubblicazione: (2025)

FedEx-LoRA: Exact Aggregation for Federated and Efficient Fine-Tuning of Foundation Models
di: Singhal, Raghav, et al.
Pubblicazione: (2024)

ADF-LoRA: Alternating Low-Rank Aggregation for Decentralized Federated Fine-Tuning
di: Wang, Xiaoyu, et al.
Pubblicazione: (2025)

CaraServe: CPU-Assisted and Rank-Aware LoRA Serving for Generative LLM Inference
di: Li, Suyi, et al.
Pubblicazione: (2024)

Predictive-LoRA: A Proactive and Fragmentation-Aware Serverless Inference System for LLMs
di: Ni, Yinan, et al.
Pubblicazione: (2025)

ForkKV: Scaling Multi-LoRA Agent Serving via Copy-on-Write Disaggregated KV Cache
di: Wang, Shao, et al.
Pubblicazione: (2026)

HLoRA: Efficient Federated Learning System for LLM Heterogeneous Fine-Tuning
di: Liu, Qianli, et al.
Pubblicazione: (2025)

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

HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models
di: Lin, Zheng, et al.
Pubblicazione: (2025)

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

Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead
di: Brüel-Gabrielsson, Rickard, et al.
Pubblicazione: (2024)

Efficient Multi-Adapter LLM Serving via Cross-Model KV-Cache Reuse with Activated LoRA
di: Li, Allison, et al.
Pubblicazione: (2025)