[HTML][HTML] Model aggregation techniques in federated learning: A comprehensive survey
Federated learning (FL) is a distributed machine learning (ML) approach that enables
models to be trained on client devices while ensuring the privacy of user data. Model …
models to be trained on client devices while ensuring the privacy of user data. Model …
Edge learning for 6g-enabled internet of things: A comprehensive survey of vulnerabilities, datasets, and defenses
The deployment of the fifth-generation (5G) wireless networks in Internet of Everything (IoE)
applications and future networks (eg, sixth-generation (6G) networks) has raised a number …
applications and future networks (eg, sixth-generation (6G) networks) has raised a number …
Blockchain-based personalized federated learning for internet of medical things
The rapid growth of artificial intelligence (AI), blockchain technology, and edge computing
services have enabled the Internet of Medical Things (IoMT) to provide various healthcare …
services have enabled the Internet of Medical Things (IoMT) to provide various healthcare …
Parallel incremental association rule mining framework for public opinion analysis
Internet public opinion association rule mining (POARM) has garnered significant attention
from a larger group of netizens. However, most POARM methods have been applied to post …
from a larger group of netizens. However, most POARM methods have been applied to post …
Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey
Due to the greatly improved capabilities of devices, massive data, and increasing concern
about data privacy, Federated Learning (FL) has been increasingly considered for …
about data privacy, Federated Learning (FL) has been increasingly considered for …
Building trusted federated learning: Key technologies and challenges
Federated learning (FL) provides convenience for cross-domain machine learning
applications and has been widely studied. However, the original FL is still vulnerable to …
applications and has been widely studied. However, the original FL is still vulnerable to …
Blockchain and federated learning-based intrusion detection approaches for edge-enabled industrial IoT networks: A survey
S Ali, Q Li, A Yousafzai - Ad Hoc Networks, 2024 - Elsevier
The industrial internet of things (IIoT) is an evolutionary extension of the traditional Internet of
Things (IoT) into processes and machines for applications in the industrial sector. The IIoT …
Things (IoT) into processes and machines for applications in the industrial sector. The IIoT …
SBPA: sybil-based backdoor poisoning attacks for distributed big data in AIoT-based federated learning system
Federated learning (FL) enables a great deal of distributed independent participants to
collaborate in training without sharing data. Malicious adversary can poison the local model …
collaborate in training without sharing data. Malicious adversary can poison the local model …
Enhancing federated learning robustness through randomization and mixture
Protecting data privacy is a significant challenge in machine learning (ML), and federated
learning (FL) has emerged as a decentralized learning solution to address this issue …
learning (FL) has emerged as a decentralized learning solution to address this issue …
FDSFL: Filtering Defense Strategies toward Targeted Poisoning Attacks in IIoT-Based Federated Learning Networking System
As a novel distributed machine learning scheme, federated learning (FL) efficiently realizes
the collaborative training of models by global participants while also protecting their data …
the collaborative training of models by global participants while also protecting their data …