Client Selection for Federated Learning in Vehicular Edge Computing: A Deep Reinforcement Learning Approach
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Access"
DOI: 10.1109/access.2024.3458991
Abstract: Vehicular edge computing (VEC) has emerged as a solution that places computing resources at the edge of the network to address resource management, service continuity, and scalability issues in dynamic vehicular environments. However, VEC faces… read more here.
Keywords: vehicular edge; client selection; client; communication ... See more keywords
VFedCS: Optimizing Client Selection for Volatile Federated Learning
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2022.3195073
Abstract: Federated learning (FL) has shown great potential as a privacy-preserving solution to training a centralized model based on local data from available clients. However, we argue that, over the course of training, the available clients… read more here.
Keywords: client selection; client; training; federated learning ... See more keywords
Client Selection in Hierarchical Federated Learning
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2024.3403082
Abstract: Federated learning (FL) is a promising technique for providing distributed learning without clients disclosing their private data. In hierarchical FL (HFL), edge servers partially aggregate the parameters of their connected clients’ models, improving scalability and… read more here.
Keywords: client selection; selection hierarchical; client; federated learning ... See more keywords
Client Selection for Wireless Federated Learning With Data and Latency Heterogeneity
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2024.3425757
Abstract: Federated learning is a distributed machine learning paradigm that allows multiple edge devices to collaboratively train a shared model without exchanging raw data. However, the training efficiency of federated learning is highly dependent on client… read more here.
Keywords: data latency; federated learning; client selection;
Two-Stage Client Selection for Federated Learning Against Free-Riding Attack: A Multiarmed Bandits and Auction-Based Approach
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2024.3431555
Abstract: Utilizing the federated learning (FL) technique, data owners can collaboratively train artificial intelligence models, retaining all training data on their premises to minimize the potential for personal data breaches. However, self-interested users (e.g., free riders)… read more here.
Keywords: auction; client selection; client; federated learning ... See more keywords
SecureFedPROM: A Zero-Trust Federated Learning Approach With Multi-Criteria Client Selection
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Journal on Selected Areas in Communications"
DOI: 10.1109/jsac.2025.3560008
Abstract: Federated Learning (FL) enables decentralized learning while preserving data privacy. However, ensuring security and optimizing resource utilization in FL remains challenging, particularly in untrusted environments. To address this, we propose SecureFedPROM, a novel zero-trust FL… read more here.
Keywords: zero trust; client; multi criteria; client selection ... See more keywords
Federated Transfer Learning With Client Selection for Intrusion Detection in Mobile Edge Computing
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Communications Letters"
DOI: 10.1109/lcomm.2022.3140273
Abstract: In this letter, we propose an efficient federated transfer learning (FTL) framework with client selection for intrusion detection (ID) in mobile edge computing (MEC). Specifically, we leverage federated learning (FL) to preserve privacy by training… read more here.
Keywords: client selection; federated transfer; transfer learning; transfer ... See more keywords
Robust Federated Learning Client Selection With Combinatorial Class Representations and Data Augmentation
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Information Forensics and Security"
DOI: 10.1109/tifs.2025.3579290
Abstract: The federated learning (FL) client selection scheme can effectively mitigate global model performance degradation caused by the random aggregation of clients with heterogeneous data. Simultaneously, research has exposed FL’s susceptibility to backdoor attacks. However herein… read more here.
Keywords: client; grace; class representations; client selection ... See more keywords
An Active Client Selection Scheme Based on Blockchain for Federated Learning in Shipping
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Intelligent Transportation Systems"
DOI: 10.1109/tits.2025.3591530
Abstract: Federated Learning (FL) enables collaborative model training across maritime devices without the need to share raw data. However, challenges such as data heterogeneity and unreliable marine communications impede its performance and security. In this work,… read more here.
Keywords: active client; selection scheme; federated learning; client selection ... See more keywords
AUCTION: Automated and Quality-Aware Client Selection Framework for Efficient Federated Learning
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Parallel and Distributed Systems"
DOI: 10.1109/tpds.2021.3134647
Abstract: The emergency of federated learning (FL) enables distributed data owners to collaboratively build a global model without sharing their raw data, which creates a new business chance for building data market. However, in practical FL… read more here.
Keywords: client selection; auction; client; quality ... See more keywords
Data-Centric Client Selection for Federated Learning Over Distributed Edge Networks
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Parallel and Distributed Systems"
DOI: 10.1109/tpds.2022.3217271
Abstract: This work presents an efficient data-centric client selection approach, named DICE, to enable federated learning (FL) over distributed edge networks. Prior research focused on assessing the computation and communication ability of the client devices for… read more here.
Keywords: client selection; client; data centric; edge ... See more keywords