Clustered Federated Learning in Heterogeneous Environment.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2023.3264740
Abstract: Federated learning (FL) is a distributed machine learning framework that allows resource-constrained clients to train a global model jointly without compromising data privacy. Although FL is widely adopted, high degrees of systems and statistical heterogeneity… read more here.
Keywords: heterogeneity; clustered federated; federated learning; statistical heterogeneity ... See more keywords
DWFed: A statistical- heterogeneity-based dynamic weighted model aggregation algorithm for federated learning
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Neurorobotics"
DOI: 10.3389/fnbot.2022.1041553
Abstract: Federated Learning is a distributed machine learning framework that aims to train a global shared model while keeping their data locally, and previous researches have empirically proven the ideal performance of federated learning methods. However,… read more here.
Keywords: dynamic weighted; federated learning; statistical heterogeneity; model ... 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;