:: Library Catalog

Image de couverture de livre

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Mei, Hanzi, Cai, Dongqi, Zhou, Ao, Wang, Shangguang, Xu, Mengwei
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning
Accès en ligne:	https://arxiv.org/abs/2408.11304
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission
par: Zhang, Zeling, et autres
Publié: (2024)

FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation
par: Zhan, Ziwei, et autres
Publié: (2024)

FwdLLM: Efficient FedLLM using Forward Gradient
par: Xu, Mengwei, et autres
Publié: (2023)

pFedMoE: Data-Level Personalization with Mixture of Experts for Model-Heterogeneous Personalized Federated Learning
par: Yi, Liping, et autres
Publié: (2024)

Recall: Empowering Multimodal Embedding for Edge Devices
par: Cai, Dongqi, et autres
Publié: (2024)

EdgeMoE: Empowering Sparse Large Language Models on Mobile Devices
par: Yi, Rongjie, et autres
Publié: (2023)

MobiEdit: Resource-efficient Knowledge Editing for Personalized On-device LLMs
par: Lu, Zhenyan, et autres
Publié: (2025)

HFedMoE: Resource-aware Heterogeneous Federated Learning with Mixture-of-Experts
par: Fang, Zihan, et autres
Publié: (2026)

pFedAFM: Adaptive Feature Mixture for Batch-Level Personalization in Heterogeneous Federated Learning
par: Yi, Liping, et autres
Publié: (2024)

FedCoE: Bridging Generalization and Personalization via Federated Coordinated Dual-level MoEs
par: Dai, Penglin, et autres
Publié: (2026)

Fed-GAME: Personalized Federated Learning with Graph Attention Mixture-of-Experts For Time-Series Forecasting
par: Li, Yi, et autres
Publié: (2026)

FLEX-MoE: Federated Mixture-of-Experts with Load-balanced Expert Assignment for Edge Computing
par: Zhang, Boyang, et autres
Publié: (2025)

Personalized Federated Learning on Heterogeneous and Long-Tailed Data via Expert Collaborative Learning
par: Lv, Fengling, et autres
Publié: (2024)

GraphMoRE: Mitigating Topological Heterogeneity via Mixture of Riemannian Experts
par: Guo, Zihao, et autres
Publié: (2024)

Accelerating Vertical Federated Learning
par: Cai, Dongqi, et autres
Publié: (2022)

FedHiP: Heterogeneity-Invariant Personalized Federated Learning Through Closed-Form Solutions
par: Tang, Jianheng, et autres
Publié: (2025)

HeterMoE: Efficient Training of Mixture-of-Experts Models on Heterogeneous GPUs
par: Wu, Yongji, et autres
Publié: (2025)

Aggregation Alignment for Federated Learning with Mixture-of-Experts under Data Heterogeneity
par: Fang, Zihan, et autres
Publié: (2026)

FedAPM: Federated Learning via ADMM with Partial Model Personalization
par: Zhu, Shengkun, et autres
Publié: (2025)

FedEL: Federated Elastic Learning for Heterogeneous Devices
par: Zhang, Letian, et autres
Publié: (2025)

FedMerge: Federated Personalization via Model Merging
par: Chen, Shutong, et autres
Publié: (2025)

FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learning
par: Tastan, Nurbek, et autres
Publié: (2024)

PhoneLM:an Efficient and Capable Small Language Model Family through Principled Pre-training
par: Yi, Rongjie, et autres
Publié: (2024)

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging
par: Li, Lujun, et autres
Publié: (2025)

Horseshoe Mixtures-of-Experts (HS-MoE)
par: Polson, Nick, et autres
Publié: (2026)

HyperMoE: Towards Better Mixture of Experts via Transferring Among Experts
par: Zhao, Hao, et autres
Publié: (2024)

HiMoE: Heterogeneity-Informed Mixture-of-Experts for Fair Spatial-Temporal Forecasting
par: Yu, Shaohan, et autres
Publié: (2024)

OrdMoE: Preference Alignment via Hierarchical Expert Group Ranking in Multimodal Mixture-of-Experts LLMs
par: Gao, Yuting, et autres
Publié: (2025)

HMoE: Heterogeneous Mixture of Experts for Language Modeling
par: Wang, An, et autres
Publié: (2024)

FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning
par: Ji, Xinyuan, et autres
Publié: (2024)

Heterogeneity-aware Personalized Federated Learning via Adaptive Dual-Agent Reinforcement Learning
par: Chen, Xi, et autres
Publié: (2025)

dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis
par: Xie, Luyuan, et autres
Publié: (2025)

MoBE: Mixture-of-Basis-Experts for Compressing MoE-based LLMs
par: Chen, Xiaodong, et autres
Publié: (2025)

$\infty$-MoE: Generalizing Mixture of Experts to Infinite Experts
par: Takashiro, Shota, et autres
Publié: (2026)

DirMoE: Dirichlet-routed Mixture of Experts
par: Vahidi, Amirhossein, et autres
Publié: (2026)

FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems
par: Li, Li, et autres
Publié: (2021)

LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades
par: Li, Yanan, et autres
Publié: (2025)

FedIN: Federated Intermediate Layers Learning for Model Heterogeneity
par: Chan, Yun-Hin, et autres
Publié: (2023)

DA-MoE: Towards Dynamic Expert Allocation for Mixture-of-Experts Models
par: Aghdam, Maryam Akhavan, et autres
Publié: (2024)

FedCAP: Robust Federated Learning via Customized Aggregation and Personalization
par: Li, Youpeng, et autres
Publié: (2024)