Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges
In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
[HTML][HTML] Security of Internet of Things (IoT) using federated learning and deep learning—Recent advancements, issues and prospects
There is a great demand for an efficient security framework which can secure IoT systems
from potential adversarial attacks. However, it is challenging to design a suitable security …
from potential adversarial attacks. However, it is challenging to design a suitable security …
A data balancing approach based on generative adversarial network
L Yuan, S Yu, Z Yang, M Duan, K Li - Future Generation Computer Systems, 2023 - Elsevier
Intrusion detection is an effective means of ensuring the proper functioning of industrial
control systems (ICSs). Most intrusion detection algorithms learn the historical ICS data to …
control systems (ICSs). Most intrusion detection algorithms learn the historical ICS data to …
ADCL: toward an adaptive network intrusion detection system using collaborative learning in IoT networks
With the widespread of cyber attacks, network intrusion detection system (NIDS) is becoming
an important and essential tool to protect Internet of Things (IoT) environments. However, it …
an important and essential tool to protect Internet of Things (IoT) environments. However, it …
Robust and secure federated learning against hybrid attacks: a generic architecture
X Hao, C Lin, W Dong, X Huang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Federated Learning (FL) enables multiple clients to collaboratively train a model without
sharing their private data. However, the deployment of FL in real-world applications is …
sharing their private data. However, the deployment of FL in real-world applications is …
Decentralized online federated g-network learning for lightweight intrusion detection
Cyberattacks are increasingly threatening net-worked systems, often with the emergence of
new types of unknown (zero-day) attacks and the rise of vulnerable devices. uch attacks can …
new types of unknown (zero-day) attacks and the rise of vulnerable devices. uch attacks can …
Game theoretic analysis of AoI efficiency for participatory and federated data ecosystems
We investigate the Age of Information (AoI) of status updates, resulting from the convergence
of multiple and federated data sources subject to both independent and voluntary …
of multiple and federated data sources subject to both independent and voluntary …
[HTML][HTML] A federated learning-based zero trust intrusion detection system for Internet of Things
The rapid expansion of Internet of Things (IoT) devices presents unique challenges in
ensuring the security and privacy of interconnected systems. As cyberattacks become more …
ensuring the security and privacy of interconnected systems. As cyberattacks become more …
A Dropout-Tolerated Privacy-Preserving Method for Decentralized Crowdsourced Federated Learning
T Chen, X Wang, HN Dai, H Yang - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
Mobile crowdsourcing federated learning (FL-MCS) allows a requester to outsource its
model-training tasks to other workers who have the desired data as well as strong …
model-training tasks to other workers who have the desired data as well as strong …
Review on Approaches of Federated Modeling in Anomaly-Based Intrusion Detection for IoT Devices
The novelty of Federated Learning (FL) has emerged as a promising alternative to
centralized machine learning systems in the context of anomaly-based intrusion detection …
centralized machine learning systems in the context of anomaly-based intrusion detection …