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Contribution‐based Federated Learning client selection

https://doi.org/10.1002/int.22879
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/int.22785openalex-snapshot
A multitarget backdooring attack on deep neural networks with random location trigger
inferredcites →https://doi.org/10.1002/int.22815openalex-snapshot
Convolutional‐capsule network for gastrointestinal endoscopy image classification
inferredcites →https://doi.org/10.1002/int.22819openalex-snapshot
A two‐phase knowledge distillation model for graph convolutional network‐based recommendation
inferredcites →https://doi.org/10.1007/s13042-020-01242-zopenalex-snapshot
Adversarial examples: attacks and defenses in the physical world
inferredcites →https://doi.org/10.1016/j.ins.2019.07.023openalex-snapshot
Multilevel similarity model for high-resolution remote sensing image registration
inferredcites →https://doi.org/10.1016/j.jnca.2020.102632openalex-snapshot
Adversarial attacks on deep-learning-based SAR image target recognition
inferredcites →https://doi.org/10.1017/9781108571401openalex-snapshot
Bandit Algorithms
inferredcites →https://doi.org/10.1109/cvpr46437.2021.01400openalex-snapshot
MagFace: A Universal Representation for Face Recognition and Quality Assessment
inferredcites →https://doi.org/10.1109/icc.2019.8761315openalex-snapshot
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge
inferredcites →https://doi.org/10.1109/icc40277.2020.9148862openalex-snapshot
Client-Edge-Cloud Hierarchical Federated Learning
inferredcites →https://doi.org/10.1109/iccworkshops49005.2020.9145118openalex-snapshot
Energy-Efficient Radio Resource Allocation for Federated Edge Learning
inferredcites →https://doi.org/10.1109/icdcs47774.2020.00049openalex-snapshot
Resource-Efficient and Convergence-Preserving Online Participant Selection in Federated Learning
inferredcites →https://doi.org/10.1109/infocom.2019.8737464openalex-snapshot
Federated Learning over Wireless Networks: Optimization Model Design and Analysis
inferredcites →https://doi.org/10.1109/infocom41043.2020.9155494openalex-snapshot
Optimizing Federated Learning on Non-IID Data with Reinforcement Learning
inferredcites →https://doi.org/10.1109/infocom42981.2021.9488679openalex-snapshot
Cost-Effective Federated Learning Design
inferredcites →https://doi.org/10.1109/jsac.2020.3036952openalex-snapshot
Fast-Convergent Federated Learning
inferredcites →https://doi.org/10.1109/tdsc.2022.3143566openalex-snapshot
Attacking Deep Reinforcement Learning With Decoupled Adversarial Policy
inferredcites →https://doi.org/10.1109/tpds.2020.3040867openalex-snapshot
Accelerating Federated Learning over Reliability-Agnostic Clients in Mobile Edge Computing Systems
inferredcites →https://doi.org/10.1109/twc.2020.3031503openalex-snapshot
Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective
inferredcites →https://doi.org/10.1145/1147954.1147956openalex-snapshot
Dependent rounding and its applications to approximation algorithms
inferredcites →https://doi.org/10.1145/3133956.3133982openalex-snapshot
Practical Secure Aggregation for Privacy-Preserving Machine Learning

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-031-29845-5_3openalex-snapshot
Client Selection Frameworks Within Federated Machine Learning: The Current Paradigm
inferred← citeshttps://doi.org/10.1007/978-3-031-78841-3_3openalex-snapshot
Client Selection in Federated Learning: Challenges, Strategies, and Contextual Considerations
inferred← citeshttps://doi.org/10.1007/978-981-95-4142-3_9openalex-snapshot
BFCSR: A Blockchain-Based Federated Learning Framework with Client Selection and Round-Based Training Scheme
inferred← citeshttps://doi.org/10.1007/978-981-97-0834-5_16openalex-snapshot
FedQL: Q-Learning Guided Aggregation for Federated Learning
inferred← citeshttps://doi.org/10.1007/s11432-022-3507-7openalex-snapshot
Model architecture level privacy leakage in neural networks
inferred← citeshttps://doi.org/10.1016/b978-0-443-13897-3.00009-6openalex-snapshot
Technical considerations of federated learning in digital healthcare systems
inferred← citeshttps://doi.org/10.1016/j.aei.2025.104083openalex-snapshot
CoMB-CS: Contribution-aware and multi-armed bandit-based client selection in federated learning for DER forecasting
inferred← citeshttps://doi.org/10.1016/j.future.2025.108138openalex-snapshot
Addressing data quality decompensation in federated learning via dynamic client selection
inferred← citeshttps://doi.org/10.1016/j.future.2025.108172openalex-snapshot
Class aware efficient client selection and multi-loss guided local performance optimization in federated learning
inferred← citeshttps://doi.org/10.1016/j.hcc.2023.100179openalex-snapshot
FedQMIX: Communication-efficient federated learning via multi-agent reinforcement learning
inferred← citeshttps://doi.org/10.1016/j.ins.2023.119360openalex-snapshot
FedRich: Towards efficient federated learning for heterogeneous clients using heuristic scheduling
inferred← citeshttps://doi.org/10.1016/j.jnca.2024.104029openalex-snapshot
Node and relevant data selection in distributed predictive analytics: A query-centric approach
inferred← citeshttps://doi.org/10.1016/j.procs.2025.02.007openalex-snapshot
Addressing Heterogeneity in Federated Learning: Challenges and Solutions for a Shared Production Environment
inferred← citeshttps://doi.org/10.1080/08839514.2026.2663618openalex-snapshot
Intelligent Aggregation Federation: A Learning-Based Metasystem for Secure and Robust Federated Learning in Finance
inferred← citeshttps://doi.org/10.1109/access.2025.3586943openalex-snapshot
Federated Learning for All: A Reinforcement Learning-Based Approach for Ensuring Fairness in Client Selection
inferred← citeshttps://doi.org/10.1109/apcc60132.2023.10460740openalex-snapshot
An Efficient Client Selection for Wireless Federated Learning
inferred← citeshttps://doi.org/10.1109/ccnc65079.2026.11366255openalex-snapshot
Adaptive Federated Reinforcement Learning for Network Management in Complex Communication Networks
inferred← citeshttps://doi.org/10.1109/cisce62493.2024.10653045openalex-snapshot
Fuzzy-Based Client Selection for Enhanced Model Convergence in Federated Learning
inferred← citeshttps://doi.org/10.1109/fllm67465.2025.11390937openalex-snapshot
The Impact of Data Similarity on Client Integration Disruption in Federated Learning
inferred← citeshttps://doi.org/10.1109/flta63145.2024.10840172openalex-snapshot
Seamless Integration: Sampling Strategies in Federated Learning Systems
inferred← citeshttps://doi.org/10.1109/hpcc64274.2024.00060openalex-snapshot
FedTA: Unsupervised Federated Prototype Learning with Temperature Adaptation
inferred← citeshttps://doi.org/10.1109/icait62580.2024.10807969openalex-snapshot
Optimal Client Selection for Wireless Federated Learning
inferred← citeshttps://doi.org/10.1109/icdew58674.2023.00029openalex-snapshot
Query-driven Edge Node Selection in Distributed Learning Environments
inferred← citeshttps://doi.org/10.1109/ijcnn64981.2025.11229376openalex-snapshot
Fair and Efficient Federated Learning Client Selection via Dynamic Contribution Evaluation
inferred← citeshttps://doi.org/10.1109/ithings-greencom-cpscom-smartdata-cybermatics60724.2023.00051openalex-snapshot
Clustering-Based Federated Learning for Heterogeneous IoT Data
inferred← citeshttps://doi.org/10.1109/jiot.2025.3558910openalex-snapshot
Optimizing Federated Learning: Addressing Key Challenges in Real-World Applications
inferred← citeshttps://doi.org/10.1109/jiot.2025.3572901openalex-snapshot
Federated Learning With Client Clustering Selection and Quality-Aware Model Aggregation (2024)
inferred← citeshttps://doi.org/10.1109/jiot.2025.3598127openalex-snapshot
GPFL: A Gradient Projection-Based Client Selection Framework for Efficient Federated Learning
inferred← citeshttps://doi.org/10.1109/jiot.2025.3617794openalex-snapshot
Human–Machine Hybrid Federated Learning: Concept and Applications
inferred← citeshttps://doi.org/10.1109/jsac.2025.3560008openalex-snapshot
SecureFedPROM: A Zero-Trust Federated Learning Approach With Multi-Criteria Client Selection
inferred← citeshttps://doi.org/10.1109/ojcoms.2024.3458088openalex-snapshot
Federated Learning: Challenges, SoTA, Performance Improvements and Application Domains
inferred← citeshttps://doi.org/10.1109/tai.2025.3571872openalex-snapshot
Robust Peer-to-Peer Federated Learning With Deep Reinforcement Learning Based Client Selection Against Data Poisoning Attacks
inferred← citeshttps://doi.org/10.1109/tmc.2024.3427709openalex-snapshot
The Analysis and Optimization of Volatile Clients in Over-the-Air Federated Learning
inferred← citeshttps://doi.org/10.1109/tmc.2025.3545140openalex-snapshot
LCEFL: A Lightweight Contribution Evaluation Approach for Federated Learning
inferred← citeshttps://doi.org/10.1109/tnse.2024.3410275openalex-snapshot
Adaptive Idle Model Fusion in Hierarchical Federated Learning for Unbalanced Edge Regions
inferred← citeshttps://doi.org/10.1109/tr.2026.3655532openalex-snapshot
A Cluster-Based Client Selection Model for Federated Learning With Heterogeneous Clients
inferred← citeshttps://doi.org/10.1145/3593434.3593438openalex-snapshot
A Systematic Literature Review on Client Selection in Federated Learning
inferred← citeshttps://doi.org/10.1145/3670689openalex-snapshot
FedREAS: A Robust Efficient Aggregation and Selection Framework for Federated Learning
inferred← citeshttps://doi.org/10.1145/3690822openalex-snapshot
RFL-LSU: A Robust Federated Learning Approach with Localized Stepwise Updates
inferred← citeshttps://doi.org/10.1145/3716870openalex-snapshot
Communication-Efficient Federated Learning for Heterogeneous Clients
inferred← citeshttps://doi.org/10.1145/3800227.3800253openalex-snapshot
Research on Federated Learning Node Selection Method Based on Reputation Mechanism
inferred← citeshttps://doi.org/10.1155/int/2675763openalex-snapshot
PFedKAN: A New Paradigm for FedKAN Client Selection Based on Non‐iid
inferred← citeshttps://doi.org/10.1155/int/3466867openalex-snapshot
IntFedSV: A Novel Participants’ Contribution Evaluation Mechanism for Federated Learning
inferred← citeshttps://doi.org/10.1609/aaaiss.v3i1.31227openalex-snapshot
Multi-Criterion Client Selection for Efficient Federated Learning
inferred← citeshttps://doi.org/10.21203/rs.3.rs-8240248/v1openalex-snapshot
Fair Client Selection Method for Federated Learning Based on Discretized Firefly Algorithm
inferred← citeshttps://doi.org/10.3233/faia230628openalex-snapshot
XFed: Improving Explainability in Federated Learning by Intersection Over Union Ratio Extended Client Selection
inferred← citeshttps://doi.org/10.3390/app14166876openalex-snapshot
Multicenter Analysis of Emergency Patient Severity through Local Model Evaluation Client Selection: Optimizing Client Selection Based on Local Model Evaluation
inferred← citeshttps://doi.org/10.3390/fi17060243openalex-snapshot
Resource Allocation for Federated Learning with Heterogeneous Computing Capability in Cloud–Edge–Client IoT Architecture
inferred← citeshttps://doi.org/10.3390/s23167235openalex-snapshot
A Comprehensive Overview of IoT-Based Federated Learning: Focusing on Client Selection Methods
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