A survey on federated learning for resource-constrained IoT devices
Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …
model by learning from multiple decentralized edge clients. FL enables on-device training …
Combining federated learning and edge computing toward ubiquitous intelligence in 6G network: Challenges, recent advances, and future directions
Full leverage of the huge volume of data generated on a large number of user devices for
providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to …
providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to …
Security and privacy-enhanced federated learning for anomaly detection in IoT infrastructures
Internet of Things (IoT) anomaly detection is significant due to its fundamental roles of
securing modern critical infrastructures, such as falsified data injection detection and …
securing modern critical infrastructures, such as falsified data injection detection and …
Fog computing approaches in IoT-enabled smart cities
These days, the development of smart cities, specifically in location-aware, latency-sensitive,
and security-crucial applications (such as emergency fire events, patient health monitoring …
and security-crucial applications (such as emergency fire events, patient health monitoring …
Backdoor attacks and countermeasures on deep learning: A comprehensive review
This work provides the community with a timely comprehensive review of backdoor attacks
and countermeasures on deep learning. According to the attacker's capability and affected …
and countermeasures on deep learning. According to the attacker's capability and affected …
VFL: A verifiable federated learning with privacy-preserving for big data in industrial IoT
Due to the strong analytical ability of big data, deep learning has been widely applied to
model on the collected data in industrial Internet of Things (IoT). However, for privacy issues …
model on the collected data in industrial Internet of Things (IoT). However, for privacy issues …
On the ICN-IoT with federated learning integration of communication: Concepts, security-privacy issues, applications, and future perspectives
The individual and integration use of the Internet of Things (IoT), Information-Centric
Networking (ICN), and Federated Learning (FL) have recently been used in several network …
Networking (ICN), and Federated Learning (FL) have recently been used in several network …
Privacy-preserving aggregation in federated learning: A survey
Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms
and growing concerns over personal data privacy, Privacy-Preserving Federated Learning …
and growing concerns over personal data privacy, Privacy-Preserving Federated Learning …
Topology-aware federated learning in edge computing: A comprehensive survey
The ultra-low latency requirements of 5G/6G applications and privacy constraints call for
distributed machine learning systems to be deployed at the edge. With its simple yet …
distributed machine learning systems to be deployed at the edge. With its simple yet …
[HTML][HTML] FedSDM: Federated learning based smart decision making module for ECG data in IoT integrated Edge–Fog–Cloud computing environments
Massive data collection in modern systems has paved the way for data-driven machine
learning, a promising technique for creating reliable and robust statistical models. By …
learning, a promising technique for creating reliable and robust statistical models. By …