Cover Image

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chen, Xiaopei, Li, Liang, Ji, Fei, Wu, Wen
Format:	Preprint
Published:	2025
Subjects:	Distributed, Parallel, and Cluster Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

RingAda: Pipelining Large Model Fine-Tuning on Edge Devices with Scheduled Layer Unfreezing
by: Li, Liang, et al.
Published: (2025)

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

Memory-Efficient Federated Fine-Tuning of Large Language Models via Layer Pruning
by: Wu, Yebo, et al.
Published: (2025)

Flexible Personalized Split Federated Learning for On-Device Fine-Tuning of Foundation Models
by: Yuan, Tianjun, et al.
Published: (2025)

Split Fine-Tuning for Large Language Models in Wireless Networks
by: Zhang, Songge, et al.
Published: (2025)

Heterogeneity-Aware Memory Efficient Federated Learning via Progressive Layer Freezing
by: Yebo, Wu, et al.
Published: (2024)

Breaking the Memory Wall for Heterogeneous Federated Learning via Model Splitting
by: Tian, Chunlin, et al.
Published: (2024)

Bridging Memory Gaps: Scaling Federated Learning for Heterogeneous Clients
by: Wu, Yebo, et al.
Published: (2024)

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

SplitLLM: Hierarchical Split Learning for Large Language Model over Wireless Network
by: Zhang, Songge, et al.
Published: (2025)

Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices
by: Liu, Jun, et al.
Published: (2024)

SplitFT: An Adaptive Federated Split Learning System For LLMs Fine-Tuning
by: Shan, Yimeng, et al.
Published: (2026)

MemAscend: System Memory Optimization for SSD-Offloaded LLM Fine-Tuning
by: Liaw, Yong-Cheng, et al.
Published: (2025)

Communication-and-Computation Efficient Split Federated Learning: Gradient Aggregation and Resource Management
by: Liang, Yipeng, et al.
Published: (2025)

FedEx: Expediting Federated Learning over Heterogeneous Mobile Devices by Overlapping and Participant Selection
by: Geng, Jiaxiang, et al.
Published: (2024)

Energy-Efficient Split Learning for Fine-Tuning Large Language Models in Edge Networks
by: Li, Zuguang, et al.
Published: (2024)

Robust Federated Fine-Tuning in Heterogeneous Networks with Unreliable Connections: An Aggregation View
by: Wang, Yanmeng, et al.
Published: (2025)

Analysis and Optimized CXL-Attached Memory Allocation for Long-Context LLM Fine-Tuning
by: Liaw, Yong-Cheng, et al.
Published: (2025)

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

Analysis and Optimization of Wireless Multimodal Federated Learning on Modal Heterogeneity
by: Han, Xuefeng, et al.
Published: (2025)

Elastic Mixture of Rank-Wise Experts for Knowledge Reuse in Federated Fine-Tuning
by: Wu, Yebo, et al.
Published: (2025)

An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU
by: Yang, Ruijia, et al.
Published: (2026)

Federated Inference for Heterogeneous LLM Communication and Collaboration
by: Chen, Zihan, et al.
Published: (2026)

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

EchoPFL: Asynchronous Personalized Federated Learning on Mobile Devices with On-Demand Staleness Control
by: Li, Xiaochen, et al.
Published: (2024)

SuperSFL: Resource-Heterogeneous Federated Split Learning with Weight-Sharing Super-Networks
by: Asif, Abdullah Al, et al.
Published: (2026)

ParallelSFL: A Novel Split Federated Learning Framework Tackling Heterogeneity Issues
by: Liao, Yunming, et al.
Published: (2024)

Communication-Efficient Model Aggregation with Layer Divergence Feedback in Federated Learning
by: Wang, Liwei, et al.
Published: (2024)

Jenga: Effective Memory Management for Serving LLM with Heterogeneity
by: Zhang, Chen, et al.
Published: (2025)

Communication-Efficient Federated Fine-Tuning
by: Theologitis, Michael, et al.
Published: (2025)

MuxTune: Efficient Multi-Task LLM Fine-Tuning in Multi-Tenant Datacenters via Spatial-Temporal Backbone Multiplexing
by: Xue, Chunyu, et al.
Published: (2026)

Federated Fine-Tuning of Sparsely-Activated Large Language Models on Resource-Constrained Devices
by: Chen, Fahao, et al.
Published: (2025)

Breaking the Memory Wall for Heterogeneous Federated Learning via Progressive Training
by: Wu, Yebo, et al.
Published: (2024)

Resource-efficient Parallel Split Learning in Heterogeneous Edge Computing
by: Zhang, Mingjin, et al.
Published: (2024)

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

TSFLora: Token-Compressed Split Fine-Tuning for Wireless Edge Networks
by: Qiang, Xianke, et al.
Published: (2026)

CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models
by: Yi, Mengjun, et al.
Published: (2025)

Energy-Efficient Wireless Federated Learning via Doubly Adaptive Quantization
by: Han, Xuefeng, et al.
Published: (2024)

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

Communication-Efficient Collaborative LLM Inference over LEO Satellite Networks
by: Zhang, Songge, et al.
Published: (2026)