Copertina

Multi-Modal Style Transfer-based Prompt Tuning for Efficient Federated Domain Generalization

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Chen, Yuliang, Lin, Xi, Wu, Jun, Cai, Xiangrui, Zhang, Qiaolun, Fan, Xichun, Xu, Jiapeng, Su, Xiu
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Distributed, Parallel, and Cluster Computing
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection
di: Yuan, Liangqi, et al.
Pubblicazione: (2024)

AdaBridge: Dynamic Data and Computation Reuse for Efficient Multi-task DNN Co-evolution in Edge Systems
di: Wang, Lehao, et al.
Pubblicazione: (2024)

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

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

PromptTuner: SLO-Aware Elastic System for LLM Prompt Tuning
di: Gao, Wei, et al.
Pubblicazione: (2026)

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

Memory-Efficient Split Federated Learning for LLM Fine-Tuning on Heterogeneous Mobile Devices
di: Chen, Xiaopei, et al.
Pubblicazione: (2025)

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

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

Low-Latency Federated Fine-Tuning for Large Language Models Over Wireless Networks
di: Pang, Zhiwen, et al.
Pubblicazione: (2026)

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

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

A Survey on Model-heterogeneous Federated Learning: Problems, Methods, and Prospects
di: Fan, Boyu, et al.
Pubblicazione: (2023)

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

ExpertWeave: Efficiently Serving Expert-Specialized Fine-Tuned Adapters at Scale
di: Shi, Ge, et al.
Pubblicazione: (2025)

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

A Knowledge Distillation-empowered Adaptive Federated Reinforcement Learning Framework for Multi-Domain IoT Applications Scheduling
di: Wang, Zhiyu, et al.
Pubblicazione: (2025)

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

Robust and Communication-Efficient Federated Domain Adaptation via Random Features
di: Feng, Zhanbo, et al.
Pubblicazione: (2023)

Flash-KMeans: Fast and Memory-Efficient Exact K-Means
di: Yang, Shuo, et al.
Pubblicazione: (2026)

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

MSAO: Adaptive Modality Sparsity-Aware Offloading with Edge-Cloud Collaboration for Efficient Multimodal LLM Inference
di: Yang, Zheming, et al.
Pubblicazione: (2026)

FedCod: An Efficient Communication Protocol for Cross-Silo Federated Learning with Coding
di: Yan, Peishen, et al.
Pubblicazione: (2024)

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

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

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

ARGO: An Auto-Tuning Runtime System for Scalable GNN Training on Multi-Core Processor
di: Lin, Yi-Chien, et al.
Pubblicazione: (2024)

HybridGen: Efficient LLM Generative Inference via CPU-GPU Hybrid Computing
di: Lin, Mao, et al.
Pubblicazione: (2026)

Domain-Adversarial Transfer Learning for Fault Root Cause Identification in Cloud Computing Systems
di: Fang, Bruce, et al.
Pubblicazione: (2025)

Boosting Multimodal Federated Learning via Chained Modality Optimization
di: Zhang, Zixin, et al.
Pubblicazione: (2026)

Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients
di: Ma, Mengmeng, et al.
Pubblicazione: (2024)

MegaScale-Omni: A Hyper-Scale, Workload-Resilient System for MultiModal LLM Training in Production
di: Xue, Chunyu, et al.
Pubblicazione: (2026)

Exploring Selective Layer Fine-Tuning in Federated Learning
di: Sun, Yuchang, et al.
Pubblicazione: (2024)

Efficient Model Compression for Hierarchical Federated Learning
di: Zhu, Xi, et al.
Pubblicazione: (2024)

Efficient Multi-Worker Selection based Distributed Swarm Learning via Analog Aggregation
di: Yao, Zhuoyu, et al.
Pubblicazione: (2025)

Energy Efficient Federated Learning with Hyperdimensional Computing (HDC)
di: Ding, Yahao, et al.
Pubblicazione: (2026)

CCRSat: A Collaborative Computation Reuse Framework for Satellite Edge Computing Networks
di: Zhang, Ye, et al.
Pubblicazione: (2025)

Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization
di: Che, Tianshi, et al.
Pubblicazione: (2023)

A Spark Optimizer for Adaptive, Fine-Grained Parameter Tuning
di: Lyu, Chenghao, et al.
Pubblicazione: (2024)

Leveraging Foundation Models for Multi-modal Federated Learning with Incomplete Modality
di: Che, Liwei, et al.
Pubblicazione: (2024)