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
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
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
Client Selection for Federated Learning With Label Noise
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Vehicular Technology"
DOI: 10.1109/tvt.2021.3131852
Abstract: Federated learning (FL) unleashes the full potential of training a global statistical model collaboratively from edge clients. In wireless FL, for the scarcity of spectrum, only a fraction of clients are capable to participate in… read more here.
Keywords: client selection; label noise; client; federated learning ... See more keywords
Data Distribution-Aware Online Client Selection Algorithm for Federated Learning in Heterogeneous Networks
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Vehicular Technology"
DOI: 10.1109/tvt.2022.3205307
Abstract: Federated learning (FL) has received significant attention as a practical alternative to traditional cloud-centric machine learning (ML). The performance (e.g., accuracy and convergence time) of FL is hampered by the selection of clients having non-independent… read more here.
Keywords: client selection; selection; convergence time;
Uplink and Downlink Decoupled 5G/B5G Vehicular Networks: A Federated Learning Assisted Client Selection Method
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Vehicular Technology"
DOI: 10.1109/tvt.2022.3207916
Abstract: Owning to the powerful support of 5G/B5G technologies, it is promising that, in a vehicular network, the ever-increasing demands for artificial intelligence applications along with privacy concerns can be fulfilled by introducing federated learning (FL)… read more here.
Keywords: client selection; client; federated learning; method ... See more keywords
Online Client Selection for Asynchronous Federated Learning With Fairness Consideration
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Wireless Communications"
DOI: 10.1109/twc.2022.3211998
Abstract: Federated learning (FL) leverages the private data and computing power of multiple clients to collaboratively train a global model. Many existing FL algorithms over wireless networks adopting synchronous model aggregation suffer from the straggler issue,… read more here.
Keywords: client selection; problem; client; training ... See more keywords
An EMD-Based Adaptive Client Selection Algorithm for Federated Learning in Heterogeneous Data Scenarios
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Plant Science"
DOI: 10.3389/fpls.2022.908814
Abstract: Federated learning is a distributed machine learning framework that enables distributed nodes with computation and storage capabilities to train a global model while keeping distributed-stored data locally. This process can promote the efficiency of modeling… read more here.
Keywords: client selection; federated learning; statistical heterogeneity;