LayerCFL: an efficient federated learning with layer-wised clustering

LayerCFL: an efficient federated learning with layer-wised clustering

Abstract Federated Learning (FL) suffers from the Non-IID problem in practice, which poses a challenge for efficient and accurate model training. To address this challenge, prior research has introduced clustered FL (CFL), which involves clustering clients and training them separately. Despite its p...

Full description

Bibliographic Details
Main Authors:	Jie Yuan, Rui Qian, Tingting Yuan, Mingliang Sun, Jirui Li, Xiaoyong Li
Format:	Article
Language:	English
Published:	SpringerOpen 2023-12-01
Series:	Cybersecurity
Subjects:	Federated learning Clustered federated learning Non-IID Layer-wised clustering
Online Access:	https://doi.org/10.1186/s42400-023-00172-x

Similar Items

Comparisons Where It Matters: Using Layer-Wise Regularization to Improve Federated Learning on Heterogeneous Data
by: Ha Min Son, et al.
Published: (2022-10-01)

FedLC: Optimizing Federated Learning in Non-IID Data via Label-Wise Clustering
by: Hunmin Lee, et al.
Published: (2023-01-01)

A Cluster-Driven Adaptive Training Approach for Federated Learning
by: Younghwan Jeong, et al.
Published: (2022-09-01)

Personalized Federated Learning Algorithm with Adaptive Clustering for Non-IID IoT Data Incorporating Multi-Task Learning and Neural Network Model Characteristics
by: Hua-Yang Hsu, et al.
Published: (2023-11-01)

Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets
by: Meenakshi Aggarwal, et al.
Published: (2023-09-01)

FLIS: Clustered Federated Learning Via Inference Similarity for Non-IID Data Distribution
by: Mahdi Morafah, et al.
Published: (2023-01-01)

A tutorial on Federated Learning methodology for indoor localization with non-IID fingerprint databases
by: Minsoo Jeong, et al.
Published: (2023-08-01)

Experimenting With Normalization Layers in Federated Learning on Non-IID Scenarios
by: Bruno Casella, et al.
Published: (2024-01-01)

A Clustered Federated Learning Method of User Behavior Analysis Based on Non-IID Data
by: Jianfei Zhang, et al.
Published: (2023-03-01)

Towards Efficient Federated Learning: Layer-Wise Pruning-Quantization Scheme and Coding Design
by: Zheqi Zhu, et al.
Published: (2023-08-01)

An Adaptive Model Filtering Algorithm Based on Grubbs Test in Federated Learning
by: Wenbin Yao, et al.
Published: (2023-04-01)

Federated Learning in Heterogeneous Wireless Networks With Adaptive Mixing Aggregation and Computation Reduction
by: Jingxin Li, et al.
Published: (2024-01-01)

Aggregation Methods Based on Quality Model Assessment for Federated Learning Applications: Overview and Comparative Analysis
by: Iuliana Bejenar, et al.
Published: (2023-11-01)

Cross-Silo, Privacy-Preserving, and Lightweight Federated Multimodal System for the Identification of Major Depressive Disorder Using Audio and Electroencephalogram
by: Chetna Gupta, et al.
Published: (2023-12-01)

A federated learning algorithm using parallel-ensemble method on non-IID datasets
by: Haoran Yu, et al.
Published: (2023-06-01)

FedSC: A federated learning algorithm based on client-side clustering
by: Zhuang Wang, et al.
Published: (2023-07-01)

On a Framework for Federated Cluster Analysis
by: Morris Stallmann, et al.
Published: (2022-10-01)

An Optimization Method for Non-IID Federated Learning Based on Deep Reinforcement Learning
by: Xutao Meng, et al.
Published: (2023-11-01)

Cluster Policy in the Russian Federation: A Сase Study of Industrial Clusters
by: Rodionova Irina, et al.
Published: (2018-04-01)

Entropy-Driven Stochastic Federated Learning in Non-IID 6G Edge-RAN
by: Brahim Aamer, et al.
Published: (2021-10-01)

Federated Distillation Methodology for Label-Based Group Structures
by: Geonhee Yang, et al.
Published: (2023-12-01)

STAFL: Staleness-Tolerant Asynchronous Federated Learning on Non-iid Dataset
by: Feng Zhu, et al.
Published: (2022-01-01)

Performance Enhancement in Federated Learning by Reducing Class Imbalance of Non-IID Data
by: Mihye Seol, et al.
Published: (2023-01-01)

C2S: Class-aware client selection for effective aggregation in federated learning
by: Mei Cao, et al.
Published: (2022-09-01)

Privacy Protection in Prosumer Energy Management Based on Federated Learning
by: Yunfeng Li, et al.
Published: (2021-01-01)

FLY-SMOTE: Re-Balancing the Non-IID IoT Edge Devices Data in Federated Learning System
by: Raneen Younis, et al.
Published: (2022-01-01)

Entropy to Mitigate Non-IID Data Problem on Federated Learning for the Edge Intelligence Environment
by: Fernanda C. Orlandi, et al.
Published: (2023-01-01)

Fedisp: an incremental subgradient-proximal-based ring-type architecture for decentralized federated learning
by: Jianjun Huang, et al.
Published: (2023-11-01)

Privacy-Enhanced Federated Learning for Non-IID Data
by: Qingjie Tan, et al.
Published: (2023-09-01)

FedRDS: Federated Learning on Non-IID Data via Regularization and Data Sharing
by: Yankai Lv, et al.
Published: (2023-12-01)

A distribution information sharing federated learning approach for medical image data
by: Leiyang Zhao, et al.
Published: (2023-03-01)

NIFL: A Statistical Measures-Based Method for Client Selection in Federated Learning
by: M'haouach Mohamed, et al.
Published: (2022-01-01)

A Federated Learning Method Based on Blockchain and Cluster Training
by: Yue Li, et al.
Published: (2023-09-01)

A composition–decomposition based federated learning
by: Chaoli Sun, et al.
Published: (2023-08-01)

Federated Multi-View Spectral Clustering
by: Hongtao Wang, et al.
Published: (2020-01-01)

Secure and decentralized federated learning framework with non-IID data based on blockchain
by: Feng Zhang, et al.
Published: (2024-03-01)

FedISM: Enhancing Data Imbalance via Shared Model in Federated Learning
by: Wu-Chun Chung, et al.
Published: (2023-05-01)

Towards personalized federated learning
by: Tan, Alysa Ziying, et al.
Published: (2024)

Personalized Federated Learning with Adaptive Feature Extraction and Category Prediction in Non-IID Datasets
by: Ying-Hsun Lai, et al.
Published: (2024-03-01)

A Personalized Federated Learning Method Based on Clustering and Knowledge Distillation
by: Jianfei Zhang, et al.
Published: (2024-02-01)