Cover Image

FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Mei, Hanzi, Cai, Dongqi, Zhou, Ao, Wang, Shangguang, Xu, Mengwei
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission
by: Zhang, Zeling, et al.
Published: (2024)

FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation
by: Zhan, Ziwei, et al.
Published: (2024)

FwdLLM: Efficient FedLLM using Forward Gradient
by: Xu, Mengwei, et al.
Published: (2023)

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

Recall: Empowering Multimodal Embedding for Edge Devices
by: Cai, Dongqi, et al.
Published: (2024)

EdgeMoE: Empowering Sparse Large Language Models on Mobile Devices
by: Yi, Rongjie, et al.
Published: (2023)

MobiEdit: Resource-efficient Knowledge Editing for Personalized On-device LLMs
by: Lu, Zhenyan, et al.
Published: (2025)

pFedAFM: Adaptive Feature Mixture for Batch-Level Personalization in Heterogeneous Federated Learning
by: Yi, Liping, et al.
Published: (2024)

HFedMoE: Resource-aware Heterogeneous Federated Learning with Mixture-of-Experts
by: Fang, Zihan, et al.
Published: (2026)

FedCoE: Bridging Generalization and Personalization via Federated Coordinated Dual-level MoEs
by: Dai, Penglin, et al.
Published: (2026)

Personalized Federated Learning on Heterogeneous and Long-Tailed Data via Expert Collaborative Learning
by: Lv, Fengling, et al.
Published: (2024)

Fed-GAME: Personalized Federated Learning with Graph Attention Mixture-of-Experts For Time-Series Forecasting
by: Li, Yi, et al.
Published: (2026)

FLEX-MoE: Federated Mixture-of-Experts with Load-balanced Expert Assignment for Edge Computing
by: Zhang, Boyang, et al.
Published: (2025)

GraphMoRE: Mitigating Topological Heterogeneity via Mixture of Riemannian Experts
by: Guo, Zihao, et al.
Published: (2024)

FedHiP: Heterogeneity-Invariant Personalized Federated Learning Through Closed-Form Solutions
by: Tang, Jianheng, et al.
Published: (2025)

FedAPM: Federated Learning via ADMM with Partial Model Personalization
by: Zhu, Shengkun, et al.
Published: (2025)

Aggregation Alignment for Federated Learning with Mixture-of-Experts under Data Heterogeneity
by: Fang, Zihan, et al.
Published: (2026)

FedMerge: Federated Personalization via Model Merging
by: Chen, Shutong, et al.
Published: (2025)

HeterMoE: Efficient Training of Mixture-of-Experts Models on Heterogeneous GPUs
by: Wu, Yongji, et al.
Published: (2025)

FedEL: Federated Elastic Learning for Heterogeneous Devices
by: Zhang, Letian, et al.
Published: (2025)

Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging
by: Li, Lujun, et al.
Published: (2025)

FedPeWS: Personalized Warmup via Subnetworks for Enhanced Heterogeneous Federated Learning
by: Tastan, Nurbek, et al.
Published: (2024)

Horseshoe Mixtures-of-Experts (HS-MoE)
by: Polson, Nick, et al.
Published: (2026)

HyperMoE: Towards Better Mixture of Experts via Transferring Among Experts
by: Zhao, Hao, et al.
Published: (2024)

OrdMoE: Preference Alignment via Hierarchical Expert Group Ranking in Multimodal Mixture-of-Experts LLMs
by: Gao, Yuting, et al.
Published: (2025)

HiMoE: Heterogeneity-Informed Mixture-of-Experts for Fair Spatial-Temporal Forecasting
by: Yu, Shaohan, et al.
Published: (2024)

HMoE: Heterogeneous Mixture of Experts for Language Modeling
by: Wang, An, et al.
Published: (2024)

Heterogeneity-aware Personalized Federated Learning via Adaptive Dual-Agent Reinforcement Learning
by: Chen, Xi, et al.
Published: (2025)

FedFixer: Mitigating Heterogeneous Label Noise in Federated Learning
by: Ji, Xinyuan, et al.
Published: (2024)

MoBE: Mixture-of-Basis-Experts for Compressing MoE-based LLMs
by: Chen, Xiaodong, et al.
Published: (2025)

DirMoE: Dirichlet-routed Mixture of Experts
by: Vahidi, Amirhossein, et al.
Published: (2026)

dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis
by: Xie, Luyuan, et al.
Published: (2025)

$\infty$-MoE: Generalizing Mixture of Experts to Infinite Experts
by: Takashiro, Shota, et al.
Published: (2026)

FedIN: Federated Intermediate Layers Learning for Model Heterogeneity
by: Chan, Yun-Hin, et al.
Published: (2023)

Accelerating Vertical Federated Learning
by: Cai, Dongqi, et al.
Published: (2022)

DA-MoE: Towards Dynamic Expert Allocation for Mixture-of-Experts Models
by: Aghdam, Maryam Akhavan, et al.
Published: (2024)

FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems
by: Li, Li, et al.
Published: (2021)

MoHETS: Long-term Time Series Forecasting with Mixture-of-Heterogeneous-Experts
by: Ortigossa, Evandro S., et al.
Published: (2026)

Heterogeneous Federated Learning via Personalized Generative Networks
by: Taghiyarrenani, Zahra, et al.
Published: (2023)

pFedLoRA: Model-Heterogeneous Personalized Federated Learning with LoRA Tuning
by: Yi, Liping, et al.
Published: (2023)