Edge computing for internet of everything: A survey
In this era of the Internet of Everything (IoE), edge computing has emerged as the critical
enabling technology to solve a series of issues caused by an increasing amount of …
enabling technology to solve a series of issues caused by an increasing amount of …
Resource-adaptive federated learning with all-in-one neural composition
Abstract Conventional Federated Learning (FL) systems inherently assume a uniform
processing capacity among clients for deployed models. However, diverse client hardware …
processing capacity among clients for deployed models. However, diverse client hardware …
Spectral co-distillation for personalized federated learning
Personalized federated learning (PFL) has been widely investigated to address the
challenge of data heterogeneity, especially when a single generic model is inadequate in …
challenge of data heterogeneity, especially when a single generic model is inadequate in …
Device-Wise Federated Network Pruning
Neural network pruning particularly channel pruning is a widely used technique for
compressing deep learning models to enable their deployment on edge devices with limited …
compressing deep learning models to enable their deployment on edge devices with limited …
On the convergence of clustered federated learning
Knowledge sharing and model personalization are essential components to tackle the non-
IID challenge in federated learning (FL). Most existing FL methods focus on two extremes: 1) …
IID challenge in federated learning (FL). Most existing FL methods focus on two extremes: 1) …
Privacy-preserving and byzantine-robust federated learning framework using permissioned blockchain
H Kasyap, S Tripathy - Expert Systems with Applications, 2024 - Elsevier
Data is readily available with the growing number of smart and IoT devices. However,
application-specific data is available in small chunks and distributed across demographics …
application-specific data is available in small chunks and distributed across demographics …
Federated learning over images: vertical decompositions and pre-trained backbones are difficult to beat
We carefully evaluate a number of algorithms for learning in a federated environment, and
test their utility for a variety of image classification tasks. We consider many issues that have …
test their utility for a variety of image classification tasks. We consider many issues that have …
Next generation federated learning for edge devices: an overview
Federated learning (FL) is a popular distributed machine learning paradigm involving
numerous edge devices with enhanced privacy protection. Recently, an extensive literature …
numerous edge devices with enhanced privacy protection. Recently, an extensive literature …
Federated learning with non-iid data: A survey
Z Lu, H Pan, Y Dai, X Si, Y Zhang - IEEE Internet of Things …, 2024 - ieeexplore.ieee.org
Federated learning (FL) is an efficient decentralized machine learning methodology for
processing nonindependent and identically distributed (non-IID) data due to geographical …
processing nonindependent and identically distributed (non-IID) data due to geographical …
HDHRFL: A hierarchical robust federated learning framework for dual-heterogeneous and noisy clients
Y Jiang, D Wang, B Song, S Luo - Future Generation Computer Systems, 2024 - Elsevier
Federated learning (FL) is a distributed machine learning approach in which many clients
contribute to learning a single global model in a privacy-preserving manner on the server …
contribute to learning a single global model in a privacy-preserving manner on the server …