Federated learning enables resource-constrained edge compute devices, such as mobile phones and IoT devices, to learn a shared model for prediction, while keeping the training …
E Diao, J Ding, V Tarokh - arXiv preprint arXiv:2106.01432, 2021 - researchgate.net
Federated Learning allows training machine learning models by using the computation and private data resources of a large number of distributed clients such as smartphones and IoT …
Federated learning (FL) is increasingly becoming the norm for training models over distributed and private datasets. Major service providers rely on FL to improve services such …
Federated learning (FL) enables collaboratively training deep learning models on decentralized data. However, there are three types of heterogeneities in FL setting bringing …
Abstract The Federated Learning (FL) paradigm is known to face challenges under heterogeneous client data. Local training on non-iid distributed data results in deflected …
G Lan, XY Liu, Y Zhang, X Wang - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Federated learning (FL) is an emerging paradigm to train a global deep neural network (DNN) model by collaborative clients that store their private data locally through the …
Federated learning (FL) is an emerging distributed machine learning (ML) paradigm with enhanced privacy, aiming to achieve a" good" ML model for as many as participants while …
Federated learning is a popular distributed machine learning paradigm with enhanced privacy. Its primary goal is learning a global model that offers good performance for the …
Federated learning (FL) has been a popular method to achieve distributed machine learning among numerous devices without sharing their data to a cloud server. FL aims to learn a …