LobRA: Multi-tenant Fine-tuning over Heterogeneous Data
Fuente:
arXiv
Saved in:
| Main Authors: | Lin, Sheng, Fu, Fangcheng, Li, Haoyang, Ge, Hao, Wang, Xuanyu, Niu, Jiawen, Tu, Yaofeng, Cui, Bin |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Hetu v2: A General and Scalable Deep Learning System with Hierarchical and Heterogeneous Single Program Multiple Data Annotations
by: Li, Haoyang, et al.
Published: (2025)
by: Li, Haoyang, et al.
Published: (2025)
Malleus: Straggler-Resilient Hybrid Parallel Training of Large-scale Models via Malleable Data and Model Parallelization
by: Li, Haoyang, et al.
Published: (2024)
by: Li, Haoyang, et al.
Published: (2024)
Hydraulis: Balancing Large Transformer Model Training via Co-designing Parallel Strategies and Data Assignment
by: Li, Haoyang, et al.
Published: (2024)
by: Li, Haoyang, et al.
Published: (2024)
HexiScale: Facilitating Large Language Model Training over Heterogeneous Hardware
by: Yan, Ran, et al.
Published: (2024)
by: Yan, Ran, et al.
Published: (2024)
Demystifying Cost-Efficiency in LLM Serving over Heterogeneous GPUs
by: Jiang, Youhe, et al.
Published: (2025)
by: Jiang, Youhe, et al.
Published: (2025)
Unleashing Efficient Asynchronous RL Post-Training via Staleness-Constrained Rollout Coordination
by: Li, Haoyang, et al.
Published: (2026)
by: Li, Haoyang, et al.
Published: (2026)
Efficient Multi-round LLM Inference over Disaggregated Serving
by: He, Wenhao, et al.
Published: (2026)
by: He, Wenhao, et al.
Published: (2026)
BOute: Cost-Efficient LLM Serving with Heterogeneous LLMs and GPUs via Multi-Objective Bayesian Optimization
by: Jiang, Youhe, et al.
Published: (2026)
by: Jiang, Youhe, et al.
Published: (2026)
Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation Models
by: Cho, Yae Jee, et al.
Published: (2024)
by: Cho, Yae Jee, et al.
Published: (2024)
Improving Automatic Parallel Training via Balanced Memory Workload Optimization
by: Wang, Yujie, et al.
Published: (2023)
by: Wang, Yujie, et al.
Published: (2023)
RBLA: Rank-Based-LoRA-Aggregation for Fine-tuning Heterogeneous Models in FLaaS
by: Chen, Shuaijun, et al.
Published: (2024)
by: Chen, Shuaijun, et al.
Published: (2024)
HLoRA: Efficient Federated Learning System for LLM Heterogeneous Fine-Tuning
by: Liu, Qianli, et al.
Published: (2025)
by: Liu, Qianli, et al.
Published: (2025)
ThunderServe: High-performance and Cost-efficient LLM Serving in Cloud Environments
by: Jiang, Youhe, et al.
Published: (2025)
by: Jiang, Youhe, et al.
Published: (2025)
TridentServe: A Stage-level Serving System for Diffusion Pipelines
by: Xia, Yifei, et al.
Published: (2025)
by: Xia, Yifei, et al.
Published: (2025)
BlockLLM: Multi-tenant Finer-grained Serving for Large Language Models
by: Hu, Bodun, et al.
Published: (2024)
by: Hu, Bodun, et al.
Published: (2024)
HAFLQ: Heterogeneous Adaptive Federated LoRA Fine-tuned LLM with Quantization
by: Su, Yang, et al.
Published: (2024)
by: Su, Yang, et al.
Published: (2024)
HC-SpMM: Accelerating Sparse Matrix-Matrix Multiplication for Graphs with Hybrid GPU Cores
by: Li, Zhonggen, et al.
Published: (2024)
by: Li, Zhonggen, et al.
Published: (2024)
LSH-MoE: Communication-efficient MoE Training via Locality-Sensitive Hashing
by: Nie, Xiaonan, et al.
Published: (2024)
by: Nie, Xiaonan, et al.
Published: (2024)
OServe: Accelerating LLM Serving via Spatial-Temporal Workload Orchestration
by: Jiang, Youhe, et al.
Published: (2026)
by: Jiang, Youhe, et al.
Published: (2026)
Efficient Long Context Fine-tuning with Chunk Flow
by: Yuan, Xiulong, et al.
Published: (2025)
by: Yuan, Xiulong, et al.
Published: (2025)
Robust Federated Fine-Tuning in Heterogeneous Networks with Unreliable Connections: An Aggregation View
by: Wang, Yanmeng, et al.
Published: (2025)
by: Wang, Yanmeng, et al.
Published: (2025)
MEMO: Fine-grained Tensor Management For Ultra-long Context LLM Training
by: Zhao, Pinxue, et al.
Published: (2024)
by: Zhao, Pinxue, et al.
Published: (2024)
LAER-MoE: Load-Adaptive Expert Re-layout for Efficient Mixture-of-Experts Training
by: Liu, Xinyi, et al.
Published: (2026)
by: Liu, Xinyi, et al.
Published: (2026)
EcoLoRA: Communication-Efficient Federated Fine-Tuning of Large Language Models
by: Liu, Han, et al.
Published: (2025)
by: Liu, Han, et al.
Published: (2025)
LoRA-C: Parameter-Efficient Fine-Tuning of Robust CNN for IoT Devices
by: Ding, Chuntao, et al.
Published: (2024)
by: Ding, Chuntao, et al.
Published: (2024)
Three Birds, One Stone: Solving the Communication-Memory-Privacy Trilemma in LLM Fine-tuning Over Wireless Networks with Zeroth-Order Optimization
by: Cai, Zhijie, et al.
Published: (2026)
by: Cai, Zhijie, et al.
Published: (2026)
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)
FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations
by: Wang, Ziyao, et al.
Published: (2024)
by: Wang, Ziyao, et al.
Published: (2024)
CLLoRA: An Approach to Measure the Effects of the Context Length for LLM Fine-Tuning
by: Zhang, Ping, et al.
Published: (2025)
by: Zhang, Ping, 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)
FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning
by: Li, Rukuo, et al.
Published: (2025)
by: Li, Rukuo, et al.
Published: (2025)
CloudQC: A Network-aware Framework for Multi-tenant Distributed Quantum Computing
by: Zhou, Ruilin, et al.
Published: (2025)
by: Zhou, Ruilin, et al.
Published: (2025)
Autopoiesis: A Self-Evolving System Paradigm for LLM Serving Under Runtime Dynamics
by: Jiang, Youhe, et al.
Published: (2026)
by: Jiang, Youhe, et al.
Published: (2026)
InfiniPipe: Elastic Pipeline Parallelism for Efficient Variable-Length Long-Context LLM Training
by: Wang, Shiju, et al.
Published: (2025)
by: Wang, Shiju, et al.
Published: (2025)
Heterogeneous Federated Fine-Tuning with Parallel One-Rank Adaptation
by: Zhang, Zikai, et al.
Published: (2026)
by: Zhang, Zikai, et al.
Published: (2026)
Cascadia: An Efficient Cascade Serving System for Large Language Models
by: Jiang, Youhe, et al.
Published: (2025)
by: Jiang, Youhe, et al.
Published: (2025)
ByteScale: Efficient Scaling of LLM Training with a 2048K Context Length on More Than 12,000 GPUs
by: Ge, Hao, et al.
Published: (2025)
by: Ge, Hao, et al.
Published: (2025)
SHE-LoRA: Selective Homomorphic Encryption for Federated Tuning with Heterogeneous LoRA
by: Liu, Jianmin, et al.
Published: (2025)
by: Liu, Jianmin, et al.
Published: (2025)
GALE: Leveraging Heterogeneous Systems for Efficient Unstructured Mesh Data Analysis
by: Liu, Guoxi, et al.
Published: (2025)
by: Liu, Guoxi, et al.
Published: (2025)
DistTrain: Addressing Model and Data Heterogeneity with Disaggregated Training for Multimodal Large Language Models
by: Zhang, Zili, et al.
Published: (2024)
by: Zhang, Zili, et al.
Published: (2024)
Similar Items
-
Hetu v2: A General and Scalable Deep Learning System with Hierarchical and Heterogeneous Single Program Multiple Data Annotations
by: Li, Haoyang, et al.
Published: (2025) -
Malleus: Straggler-Resilient Hybrid Parallel Training of Large-scale Models via Malleable Data and Model Parallelization
by: Li, Haoyang, et al.
Published: (2024) -
Hydraulis: Balancing Large Transformer Model Training via Co-designing Parallel Strategies and Data Assignment
by: Li, Haoyang, et al.
Published: (2024) -
HexiScale: Facilitating Large Language Model Training over Heterogeneous Hardware
by: Yan, Ran, et al.
Published: (2024) -
Demystifying Cost-Efficiency in LLM Serving over Heterogeneous GPUs
by: Jiang, Youhe, et al.
Published: (2025)