Cover Image

Sparse Training for Federated Learning with Regularized Error Correction

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Greidi, Ran, Cohen, Kobi
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Artificial Intelligence Distributed, Parallel, and Cluster Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Gradient Correction in Federated Learning with Adaptive Optimization
by: Chen, Evan, et al.
Published: (2025)

Convergence Analysis of Federated Learning Methods Using Backward Error Analysis
by: Lim, Jinwoo, et al.
Published: (2025)

SSFL: Discovering Sparse Unified Subnetworks at Initialization for Efficient Federated Learning
by: Ohib, Riyasat, et al.
Published: (2024)

SpaFL: Communication-Efficient Federated Learning with Sparse Models and Low computational Overhead
by: Kim, Minsu, et al.
Published: (2024)

Compare Where It Matters: Using Layer-Wise Regularization To Improve Federated Learning on Heterogeneous Data
by: Son, Ha Min, et al.
Published: (2021)

FRAIN to Train: A Fast-and-Reliable Solution for Decentralized Federated Learning
by: Park, Sanghyeon, et al.
Published: (2025)

FedFa: A Fully Asynchronous Training Paradigm for Federated Learning
by: Xu, Haotian, et al.
Published: (2024)

EASTER: Embedding Aggregation-based Heterogeneous Models Training in Vertical Federated Learning
by: Wang, Shuo, et al.
Published: (2023)

Training Heterogeneous Client Models using Knowledge Distillation in Serverless Federated Learning
by: Chadha, Mohak, et al.
Published: (2024)

SEAFL: Enhancing Efficiency in Semi-Asynchronous Federated Learning through Adaptive Aggregation and Selective Training
by: Islam, Md Sirajul, et al.
Published: (2025)

FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models
by: Yi, Kai, et al.
Published: (2024)

Federated Learning with Workload Reduction through Partial Training of Client Models and Entropy-Based Data Selection
by: Shi, Hongrui, et al.
Published: (2024)

FedPBS: Proximal-Balanced Scaling Federated Learning Model for Robust Personalized Training for Non-IID Data
by: AbouNassar, Eman M., et al.
Published: (2026)

Federated Multi-Objective Learning
by: Yang, Haibo, et al.
Published: (2023)

Federated Learning with Flexible Architectures
by: Park, Jong-Ik, et al.
Published: (2024)

On Using Large-Batches in Federated Learning
by: Tyagi, Sahil
Published: (2025)

Tackling Selfish Clients in Federated Learning
by: Augello, Andrea, et al.
Published: (2024)

DFML: Decentralized Federated Mutual Learning
by: Khalil, Yasser H., et al.
Published: (2024)

Federated Learning with Limited Node Labels
by: Tang, Bisheng, et al.
Published: (2024)

Uncertainty-Aware Explainable Federated Learning
by: Zhang, Yanci, et al.
Published: (2025)

Efficient Client Selection in Federated Learning
by: Marfo, William, et al.
Published: (2025)

Influence-oriented Personalized Federated Learning
by: Tan, Yue, et al.
Published: (2024)

Variational Bayes for Federated Continual Learning
by: Yao, Dezhong, et al.
Published: (2024)

Efficient Asynchronous Federated Learning with Sparsification and Quantization
by: Jia, Juncheng, et al.
Published: (2023)

Efficient Federated Learning with Timely Update Dissemination
by: Jia, Juncheng, et al.
Published: (2025)

On the Byzantine-Resilience of Distillation-Based Federated Learning
by: Roux, Christophe, et al.
Published: (2024)

Federated Graph Learning with Structure Proxy Alignment
by: Fu, Xingbo, et al.
Published: (2024)

Privacy in Federated Learning with Spiking Neural Networks
by: Aksu, Dogukan, et al.
Published: (2025)

Clustered Federated Learning with Hierarchical Knowledge Distillation
by: Ahmad, Sabtain, et al.
Published: (2025)

One-Shot Federated Learning with Bayesian Pseudocoresets
by: d'Hondt, Tim, et al.
Published: (2024)

Learn How to Query from Unlabeled Data Streams in Federated Learning
by: Sun, Yuchang, et al.
Published: (2024)

CommunityAI: Towards Community-based Federated Learning
by: Murturi, Ilir, et al.
Published: (2023)

Delayed Random Partial Gradient Averaging for Federated Learning
by: Hu, Xinyi
Published: (2024)

Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout
by: Liu, Ji, et al.
Published: (2025)

FedMT: Federated Learning with Mixed-type Labels
by: Zhang, Qiong, et al.
Published: (2022)

FedAH: Aggregated Head for Personalized Federated Learning
by: Zhou, Pengzhan, et al.
Published: (2024)

QFed: Parameter-Compact Quantum-Classical Federated Learning
by: Abdelghani, Samar, et al.
Published: (2026)

Beyond Aggregation: Guiding Clients in Heterogeneous Federated Learning
by: Wang, Zijian, et al.
Published: (2025)

AugFL: Augmenting Federated Learning with Pretrained Models
by: Yue, Sheng, et al.
Published: (2025)

FedTLU: Federated Learning with Targeted Layer Updates
by: Park, Jong-Ik, et al.
Published: (2024)