Moving Target Defense Meets Artificial Intelligence-Driven Network: A Comprehensive Survey
Based on emerging Artificial Intelligence (AI) tasks, cloud-edge-terminal architecture can
provide powerful computing, intelligent interconnection, and real-time response, which can …
provide powerful computing, intelligent interconnection, and real-time response, which can …
Privacy-preserving federated learning of remote sensing image classification with dishonest majority
J Zhu, J Wu, AK Bashir, Q Pan… - IEEE Journal of Selected …, 2023 - ieeexplore.ieee.org
The classification of remote sensing images can give valuable data for various practical
applications for smart cities, including urban planning, construction, and water resource …
applications for smart cities, including urban planning, construction, and water resource …
Fedcomm: A privacy-enhanced and efficient authentication protocol for federated learning in vehicular ad-hoc networks
In vehicular ad-hoc networks (VANET), federated learning enables vehicles to
collaboratively train a global model for intelligent transportation without sharing their local …
collaboratively train a global model for intelligent transportation without sharing their local …
Addressing Bias and Fairness Using Fair Federated Learning: A Synthetic Review
D Kim, H Woo, Y Lee - Electronics, 2024 - search.proquest.com
The rapid increase in data volume and variety within the field of machine learning
necessitates ethical data utilization and adherence to strict privacy protection standards. Fair …
necessitates ethical data utilization and adherence to strict privacy protection standards. Fair …
A multifaceted survey on federated learning: Fundamentals, paradigm shifts, practical issues, recent developments, partnerships, trade-offs, trustworthiness, and ways …
A Majeed, SO Hwang - IEEE Access, 2024 - ieeexplore.ieee.org
Federated learning (FL) is considered a de facto standard for privacy preservation in AI
environments because it does not require data to be aggregated in some central place to …
environments because it does not require data to be aggregated in some central place to …
FL2DP: Privacy-Preserving Federated Learning via Differential Privacy for Artificial IoT
Federated learning (FL) is a promising paradigm for collaboratively training networks on
distributed clients while retaining data locally. Recent work has shown that personal data …
distributed clients while retaining data locally. Recent work has shown that personal data …
MR-FFL: A Stratified Community-Based Mutual Reliability Framework for Fairness-Aware Federated Learning in Heterogeneous UAV Networks
Fairness-aware federated learning (FFL) plays a crucial role in mitigating bias against
specific demographic groups (eg, gender, race, and occupation) during collaborative …
specific demographic groups (eg, gender, race, and occupation) during collaborative …
SHFL: Secure Hierarchical Federated Learning Framework for Edge Networks
Federated Learning (FL) is a distributed machine learning paradigm designed for privacy-
sensitive applications that run on resource-constrained devices with non-Identically and …
sensitive applications that run on resource-constrained devices with non-Identically and …
Safeguarding Privacy and Integrity of Federated Learning in Heterogeneous Cross-Silo IoRT Environments: A Moving Target Defense Approach
Bridging the gap between the Internet of Things and collaborative robots, the recent
advancements in the Internet of Robotic Things (IoRT) aim at significantly improving …
advancements in the Internet of Robotic Things (IoRT) aim at significantly improving …
An Robust Secure Blockchain-based Hierarchical Asynchronous Federated Learning Scheme for Internet of Things
Y Chen, L Yan, D Ai - IEEE Access, 2024 - ieeexplore.ieee.org
Combining the Internet of Things (IoT) and federated learning (FL) is a trend. In addition to
privacy risks, a long-term operating IoT always faces a hierarchical environment …
privacy risks, a long-term operating IoT always faces a hierarchical environment …