Efficient parallel split learning over resource-constrained wireless edge networks
The increasingly deeper neural networks hinder the democratization of privacy-enhancing
distributed learning, such as federated learning (FL), to resource-constrained devices. To …
distributed learning, such as federated learning (FL), to resource-constrained devices. To …
A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions
Internet of Things (IoT) devices often operate with limited resources while interacting with
users and their environment, generating a wealth of data. Machine learning models interpret …
users and their environment, generating a wealth of data. Machine learning models interpret …
Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach
Federated learning (FL) is a viable technique to train a shared machine learning model
without sharing data. Hierarchical FL (HFL) system has yet to be studied regrading its …
without sharing data. Hierarchical FL (HFL) system has yet to be studied regrading its …
Distributed traffic synthesis and classification in edge networks: A federated self-supervised learning approach
With the rising demand for wireless services and increased awareness of the need for data
protection, existing network traffic analysis and management architectures are facing …
protection, existing network traffic analysis and management architectures are facing …
Federated split learning for edge intelligence in resource-constrained wireless networks
H Ao, H Tian, W Ni - IEEE Transactions on Consumer …, 2024 - ieeexplore.ieee.org
The rapid advancement of the Internet of Things (IoT) and artificial intelligence has
significantly increased the number of consumer electronics devices. Traditional federated …
significantly increased the number of consumer electronics devices. Traditional federated …
Towards net-zero carbon emissions in network AI for 6G and beyond
A global effort has been initiated to reduce the worldwide greenhouse gas (GHG) emissions,
primarily carbon emissions, by half by 2030 and reach net-zero by 2050. The development …
primarily carbon emissions, by half by 2030 and reach net-zero by 2050. The development …
LightFIDS: Lightweight and Hierarchical Federated IDS for Massive IoT in 6G Network
A Alotaibi, A Barnawi - Arabian Journal for Science and Engineering, 2024 - Springer
IoT traffic on access networks is expected to increase significantly with the advent of 6G-
enabled massive IoT networks. Nevertheless, current intrusion detection system (IDS) …
enabled massive IoT networks. Nevertheless, current intrusion detection system (IDS) …
Distributed Optimization of Resource-Efficiency for Federated Edge Intelligence in UAV-Enabled IoT Networks
Y Li, J Rao, L Wang, Y **ao, X Ge… - IEEE Internet of Things …, 2025 - ieeexplore.ieee.org
UAV-enabled IoT networks have shown promising potential in a range of novel applications
and service scenarios such as extending the network coverage, extending the battery …
and service scenarios such as extending the network coverage, extending the battery …
Adaptive Clustering based Straggler-aware Federated Learning in Wireless Edge Networks
Federated learning (FL) has been vigorously promoted in wireless edge networks as it
fosters collaborative training of machine learning (ML) models while preserving individual …
fosters collaborative training of machine learning (ML) models while preserving individual …
Reinforcement Learning for Real-Time Federated Learning for Resource-Constrained Edge Cluster
For performing various predictive analytics tasks for real-time mission-critical applications,
Federated Learning (FL) have emerged as the go-to machine learning paradigm for its …
Federated Learning (FL) have emerged as the go-to machine learning paradigm for its …