Imagen de Portada

Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Zhang, Jiaojiao, Hu, Jiang, So, Anthony Man-Cho, Johansson, Mikael
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning Distributed, Parallel, and Cluster Computing
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Non-convex composite federated learning with heterogeneous data
por: Zhang, Jiaojiao, et al.
Publicado: (2025)

Locally Differentially Private Online Federated Learning With Correlated Noise
por: Zhang, Jiaojiao, et al.
Publicado: (2024)

Differentially Private Online Federated Learning with Correlated Noise
por: Zhang, Jiaojiao, et al.
Publicado: (2024)

An Efficient Subspace Algorithm for Federated Learning on Heterogeneous Data
por: Zhang, Jiaojiao, et al.
Publicado: (2025)

From promise to practice: realizing high-performance decentralized training
por: Wang, Zesen, et al.
Publicado: (2024)

Controlled disagreement improves generalization in decentralized training
por: Wang, Zesen, et al.
Publicado: (2026)

Hypernetworks for Model-Heterogeneous Personalized Federated Learning
por: Zhang, Chen, et al.
Publicado: (2025)

Convergence Analysis of Split Federated Learning on Heterogeneous Data
por: Han, Pengchao, et al.
Publicado: (2024)

Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data
por: Chen, Yiyue, et al.
Publicado: (2025)

Efficient Federated Learning against Byzantine Attacks and Data Heterogeneity via Aggregating Normalized Gradients
por: Zuo, Shiyuan, et al.
Publicado: (2024)

Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited Staleness
por: Wang, Haoming, et al.
Publicado: (2023)

Tackling Privacy Heterogeneity in Differentially Private Federated Learning
por: Xu, Ruichen, et al.
Publicado: (2026)

HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learning
por: Kim, Gyudong, et al.
Publicado: (2024)

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

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

Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients
por: Wang, Xiaolu, et al.
Publicado: (2024)

SMoFi: Step-wise Momentum Fusion for Split Federated Learning on Heterogeneous Data
por: Yang, Mingkun, et al.
Publicado: (2025)

Tackling Resource-Constrained and Data-Heterogeneity in Federated Learning with Double-Weight Sparse Pack
por: Yang, Qiantao, et al.
Publicado: (2026)

Federated Model Heterogeneous Matryoshka Representation Learning
por: Yi, Liping, et al.
Publicado: (2024)

FLASH: Federated Learning Across Simultaneous Heterogeneities
por: Chang, Xiangyu, et al.
Publicado: (2024)

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

STHFL: Spatio-Temporal Heterogeneous Federated Learning
por: Guo, Shunxin, et al.
Publicado: (2025)

AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems
por: Jia, Chentao, et al.
Publicado: (2023)

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

Heterogeneous Federated Learning with Convolutional and Spiking Neural Networks
por: Yu, Yingchao, et al.
Publicado: (2024)

Heterogeneity: An Open Challenge for Federated On-board Machine Learning
por: Hartmann, Maria, et al.
Publicado: (2024)

Knowledge-Driven Federated Graph Learning on Model Heterogeneity
por: Wu, Zhengyu, et al.
Publicado: (2025)

Federated Deep Equilibrium Learning: Harnessing Compact Global Representations to Enhance Personalization
por: Le, Long Tan, et al.
Publicado: (2023)

Heterogeneity-Aware Cooperative Federated Edge Learning with Adaptive Computation and Communication Compression
por: Zhang, Zhenxiao, et al.
Publicado: (2024)

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

CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated Active Learning
por: Zhang, Jun, et al.
Publicado: (2025)

Dual-Distilled Heterogeneous Federated Learning with Adaptive Margins for Trainable Global Prototypes
por: Siddika, Fatema, et al.
Publicado: (2025)

Non-Federated Multi-Task Split Learning for Heterogeneous Sources
por: Zheng, Yilin, et al.
Publicado: (2024)

Apodotiko: Enabling Efficient Serverless Federated Learning in Heterogeneous Environments
por: Chadha, Mohak, et al.
Publicado: (2024)

On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments
por: Wang, Leo Muxing, et al.
Publicado: (2024)

Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizations
por: Shao, Yumeng, et al.
Publicado: (2024)

Heterogeneity-Aware Client Selection Methodology For Efficient Federated Learning
por: Balivada, Nihal, et al.
Publicado: (2026)

Resource-Aware Aggregation and Sparsification in Heterogeneous Ensemble Federated Learning
por: Ryum, Keumseo, et al.
Publicado: (2025)

Federated Learning under Periodic Client Participation and Heterogeneous Data: A New Communication-Efficient Algorithm and Analysis
por: Crawshaw, Michael, et al.
Publicado: (2024)

Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation
por: Zhang, Zikai, et al.
Publicado: (2025)