Imagen de Portada

Contribution Evaluation of Heterogeneous Participants in Federated Learning via Prototypical Representations

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Guo, Qi, Yao, Minghao, Tian, Zhen, Qi, Saiyu, Qi, Yong, Lin, Yun, Dong, Jin Song
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Forgetting Through Transforming: Enabling Federated Unlearning via Class-Aware Representation Transformation
por: Guo, Qi, et al.
Publicado: (2024)

Knowledge Rumination for Client Utility Evaluation in Heterogeneous Federated Learning
por: Jiang, Xiaorui, et al.
Publicado: (2023)

How to Evaluate Participant Contributions in Decentralized Federated Learning
por: Anada, Honoka, et al.
Publicado: (2025)

Representation Retrieval Learning for Heterogeneous Data Integration
por: Xu, Qi, et al.
Publicado: (2025)

Adaptive Prototype Knowledge Transfer for Federated Learning with Mixed Modalities and Heterogeneous Tasks
por: Gai, Keke, et al.
Publicado: (2025)

Vertical Federated Continual Learning via Evolving Prototype Knowledge
por: Wang, Shuo, et al.
Publicado: (2025)

Prototype-based Heterogeneous Federated Learning for Blade Icing Detection in Wind Turbines with Class Imbalanced Data
por: Qi, Lele, et al.
Publicado: (2025)

FedRE: A Representation Entanglement Framework for Model-Heterogeneous Federated Learning
por: Yao, Yuan, et al.
Publicado: (2025)

Addressing Skewed Heterogeneity via Federated Prototype Rectification with Personalization
por: Guo, Shunxin, et al.
Publicado: (2024)

Heterogeneity-Oblivious Robust Federated Learning
por: Zhang, Weiyao, et al.
Publicado: (2025)

FedORGP: Guiding Heterogeneous Federated Learning with Orthogonality Regularization on Global Prototypes
por: Guo, Fucheng, et al.
Publicado: (2025)

SFedHIFI: Fire Rate-Based Heterogeneous Information Fusion for Spiking Federated Learning
por: Tao, Ran, et al.
Publicado: (2026)

Detecting High-Potential SMEs with Heterogeneous Graph Neural Networks
por: Qi, Yijiashun, et al.
Publicado: (2026)

Feature Distillation is the Better Choice for Model-Heterogeneous Federated Learning
por: Li, Yichen, et al.
Publicado: (2025)

CO-PFL: Contribution-Oriented Personalized Federated Learning for Heterogeneous Networks
por: Xing, Ke, et al.
Publicado: (2025)

ST-ReP: Learning Predictive Representations Efficiently for Spatial-Temporal Forecasting
por: Zheng, Qi, et al.
Publicado: (2024)

Enhancing Visual Representation with Textual Semantics: Textual Semantics-Powered Prototypes for Heterogeneous Federated Learning
por: Wu, Xinghao, et al.
Publicado: (2025)

PTaRL: Prototype-based Tabular Representation Learning via Space Calibration
por: Ye, Hangting, et al.
Publicado: (2024)

Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains
por: Wang, Lei, et al.
Publicado: (2024)

Heterogeneous Federated Learning with Prototype Alignment and Upscaling
por: Lee, Gyuejeong, et al.
Publicado: (2025)

Enabling Weak Client Participation via On-device Knowledge Distillation in Heterogeneous Federated Learning
por: Lim, Jihyun, et al.
Publicado: (2025)

Multimodal Federated Learning with Missing Modality via Prototype Mask and Contrast
por: Bao, Guangyin, et al.
Publicado: (2023)

pFedFair: Towards Optimal Group Fairness-Accuracy Trade-off in Heterogeneous Federated Learning
por: Lei, Haoyu, et al.
Publicado: (2025)

The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy
por: Ma, Jiating, et al.
Publicado: (2024)

Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data
por: Guo, Wei, et al.
Publicado: (2025)

Customized Multiple Clustering via Multi-Modal Subspace Proxy Learning
por: Yao, Jiawei, et al.
Publicado: (2024)

Federated Learning with Enhanced Privacy via Model Splitting and Random Client Participation
por: Li, Yiwei, et al.
Publicado: (2025)

Bohdi: Heterogeneous LLM Fusion with Automatic Data Exploration
por: Gao, Junqi, et al.
Publicado: (2025)

FedDPC : Handling Data Heterogeneity and Partial Client Participation in Federated Learning
por: Sen, Mrinmay, et al.
Publicado: (2025)

FedLAD: A Linear Algebra Based Data Poisoning Defence for Federated Learning
por: Xiong, Qi, et al.
Publicado: (2025)

GraphVec: Cross-Domain Graph Vectorization for Graph-Level Representation Learning
por: Feng, Qi, et al.
Publicado: (2026)

Generative Learning of Heterogeneous Tail Dependence
por: Sun, Xiangqian, et al.
Publicado: (2020)

MCLRL: A Multi-Domain Contrastive Learning with Reinforcement Learning Framework for Few-Shot Modulation Recognition
por: Xu, Dongwei, et al.
Publicado: (2025)

Multi-task Learning for Heterogeneous Data via Integrating Shared and Task-Specific Encodings
por: Sui, Yang, et al.
Publicado: (2025)

Federated Learning for Heterogeneous Electronic Health Record Systems with Cost Effective Participant Selection
por: Kim, Jiyoun, et al.
Publicado: (2024)

Exploiting Features and Logits in Heterogeneous Federated Learning
por: Chan, Yun-Hin, et al.
Publicado: (2022)

Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs
por: Zhang, Tiehua, et al.
Publicado: (2023)

Generalist Graph Anomaly Detection via Prototype-Based Distillation
por: Xu, Yiming, et al.
Publicado: (2026)

FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning
por: Zhou, Yanbing, et al.
Publicado: (2025)

Federated Reinforcement Learning with Constraint Heterogeneity
por: Jin, Hao, et al.
Publicado: (2024)