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 …
A novel deep federated learning-based model to enhance privacy in critical infrastructure systems
Deep learning (DL) can provide critical infrastructure operators with valuable insights and
predictive capabilities to help them make more informed decisions, improving system's …
predictive capabilities to help them make more informed decisions, improving system's …
Secure Video Offloading in MEC-Enabled IIoT Networks: A Multicell Federated Deep Reinforcement Learning Approach
Wireless video offloading in mobile-edge-computing (MEC)-enabled Industrial Internet of
Things imposes a risk of exposing users' private data to eavesdroppers. It is difficult for …
Things imposes a risk of exposing users' private data to eavesdroppers. It is difficult for …
Corrfl: correlation-based neural network architecture for unavailability concerns in a heterogeneous iot environment
The Federated Learning (FL) paradigm faces several challenges that limit its application in
real-world environments. These challenges include the local models' architecture …
real-world environments. These challenges include the local models' architecture …
Edge-Cloud Architectures for Hybrid Energy Management Systems: A Comprehensive Review
This article provides an overview of recent research on edge-cloud architectures in hybrid
energy management systems (HEMSs). It delves into the typical structure of an IoT system …
energy management systems (HEMSs). It delves into the typical structure of an IoT system …
A Systematic Review of Federated Generative Models
Federated Learning (FL) has emerged as a solution for distributed systems that allow clients
to train models on their data and only share models instead of local data. Generative Models …
to train models on their data and only share models instead of local data. Generative Models …
Efficient privacy-preserving ML for IoT: Cluster-based split federated learning scheme for non-IID data
In this paper, we propose a solution to address the challenges of varying client resource
capabilities in the IoT environment when using the SplitFed architecture for training models …
capabilities in the IoT environment when using the SplitFed architecture for training models …
On the impact of data heterogeneity in federated learning environments with application to healthcare networks
Federated Learning (FL) allows multiple privacy-sensitive applications to leverage their
dataset for a global model construction without any disclosure of the information. One of …
dataset for a global model construction without any disclosure of the information. One of …
Confidentiality preserved federated learning for indoor localization using wi-fi fingerprinting
For the establishment of future ubiquitous location-aware applications, a scalable indoor
localization technique is essential technology. Numerous classification techniques for indoor …
localization technique is essential technology. Numerous classification techniques for indoor …
Big data innovations in enterprise information systems: strategies formation for new generation entrepreneurs
BB Gupta, A Gaurav, V Arya… - Enterprise Information …, 2025 - Taylor & Francis
Big data's inclusion into enterprise information systems (EIS) has transformed corporate
strategies. Therefore, it supports data-driven innovation and decision-making. Focusing on …
strategies. Therefore, it supports data-driven innovation and decision-making. Focusing on …