- Home
- Publications
- Publication Search
- Publication Details
Title
A Review of Client Selection Methods in Federated Learning
Authors
Keywords
-
Journal
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2023-11-01
DOI
10.1007/s11831-023-10011-4
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Federated Learning in Edge Computing: A Systematic Survey
- (2022) Haftay Gebreslasie Abreha et al. SENSORS
- Federated learning with workload-aware client scheduling in heterogeneous systems
- (2022) Li Li et al. NEURAL NETWORKS
- A state-of-the-art survey on solving non-IID data in Federated Learning
- (2022) Xiaodong Ma et al. Future Generation Computer Systems-The International Journal of eScience
- A Contribution-Based Device Selection Scheme in Federated Learning
- (2022) Shashi Raj Pandey et al. IEEE COMMUNICATIONS LETTERS
- Federated learning review: Fundamentals, enabling technologies, and future applications
- (2022) Syreen Banabilah et al. INFORMATION PROCESSING & MANAGEMENT
- Stochastic Client Selection for Federated Learning With Volatile Clients
- (2022) Tiansheng Huang et al. IEEE Internet of Things Journal
- Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoT
- (2022) Zonghang Li et al. IEEE Internet of Things Journal
- Context-Aware Online Client Selection for Hierarchical Federated Learning
- (2022) Zhe Qu et al. IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
- VFedCS: Optimizing Client Selection for Volatile Federated Learning
- (2022) Fang Shi et al. IEEE Internet of Things Journal
- A survey on federated learning
- (2021) Chen Zhang et al. KNOWLEDGE-BASED SYSTEMS
- Edge computing-based joint client selection and networking scheme for federated learning in vehicular IoT
- (2021) Wugedele Bao et al. China Communications
- Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges
- (2021) Dinh C. Nguyen et al. IEEE Internet of Things Journal
- Online Client Scheduling for Fast Federated Learning
- (2021) Bo Xu et al. IEEE Wireless Communications Letters
- Federated learning on non-IID data: A survey
- (2021) Hangyu Zhu et al. NEUROCOMPUTING
- Challenges and future directions of secure federated learning: a survey
- (2021) Kaiyue Zhang et al. Frontiers of Computer Science
- A Survey on Federated Learning for Resource-Constrained IoT Devices
- (2021) Ahmed Imteaj et al. IEEE Internet of Things Journal
- Federated Learning for 6G: Applications, Challenges, and Opportunities
- (2021) Zhaohui Yang et al. Engineering
- Jointly Optimizing Client Selection and Resource Management in Wireless Federated Learning for Internet of Things
- (2021) Liangkun Yu et al. IEEE Internet of Things Journal
- Vehicle Selection and Resource Optimization for Federated Learning in Vehicular Edge Computing
- (2021) Huizi Xiao et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- AUCTION: Automated and Quality-Aware Client Selection Framework for Efficient Federated Learning
- (2021) Yongheng Deng et al. IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
- A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection
- (2021) Qinbin Li et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
- Joint Client Selection and Bandwidth Allocation Algorithm for Federated Learning
- (2021) Haneul Ko et al. IEEE TRANSACTIONS ON MOBILE COMPUTING
- Federated Learning: Challenges, Methods, and Future Directions
- (2020) Tian Li et al. IEEE SIGNAL PROCESSING MAGAZINE
- Optimal User Selection for High-Performance and Stabilized Energy-Efficient Federated Learning Platforms
- (2020) Joohyung Jeon et al. Electronics
- A survey on security and privacy of federated learning
- (2020) Viraaji Mothukuri et al. Future Generation Computer Systems-The International Journal of eScience
- FedMCCS: Multicriteria Client Selection Model for Optimal IoT Federated Learning
- (2020) Sawsan Abdulrahman et al. IEEE Internet of Things Journal
- SAFA: A Semi-Asynchronous Protocol for Fast Federated Learning With Low Overhead
- (2020) Wentai Wu et al. IEEE TRANSACTIONS ON COMPUTERS
- Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learning
- (2020) Ihab Mohammed et al. IEEE Internet of Things Journal
- A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond
- (2020) Sawsan Abdulrahman et al. IEEE Internet of Things Journal
- Federated Machine Learning
- (2019) Qiang Yang et al. ACM Transactions on Intelligent Systems and Technology
- Budget-Constrained Edge Service Provisioning With Demand Estimation via Bandit Learning
- (2019) Lixing Chen et al. IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
- Combinatorial Sleeping Bandits With Fairness Constraints
- (2019) Fengjiao Li et al. IEEE Transactions on Network Science and Engineering
- Combining similarity and sentiment in opinion mining for product recommendation
- (2015) Ruihai Dong et al. JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
- Privacy Preserving Back-Propagation Neural Network Learning Made Practical with Cloud Computing
- (2013) Jiawei Yuan et al. IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreAdd your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload Now