A Survey on Heterogeneity Taxonomy, Security and Privacy Preservation in the Integration of IoT, Wireless Sensor Networks and Federated Learning
TM Mengistu, T Kim, JW Lin - Sensors, 2024 - mdpi.com
Federated learning (FL) is a machine learning (ML) technique that enables collaborative
model training without sharing raw data, making it ideal for Internet of Things (IoT) …
model training without sharing raw data, making it ideal for Internet of Things (IoT) …
Dynamic user clustering for efficient and privacy-preserving federated learning
With the wider adoption of machine learning and increasing concern about data privacy,
federated learning (FL) has received tremendous attention. FL schemes typically enable a …
federated learning (FL) has received tremendous attention. FL schemes typically enable a …
[HTML][HTML] Federated learning enables 6 G communication technology: Requirements, applications, and integrated with intelligence framework
The 5 G networks are effectively deployed worldwide, and academia and industries have
begun looking at 6 G network communication technology for consumer electronics …
begun looking at 6 G network communication technology for consumer electronics …
Semi-federated learning for connected intelligence with computing-heterogeneous devices
J Han, W Ni, L Li - IEEE Internet of Things Journal, 2024 - ieeexplore.ieee.org
Federated learning (FL) is a promising distributed learning approach which enables multiple
devices to collaboratively train deep neural networks in a privacy-preserving fashion …
devices to collaboratively train deep neural networks in a privacy-preserving fashion …
User assignment and resource allocation for hierarchical federated learning over wireless networks
The large population of wireless users is a key driver of data-crowdsourced Machine
Learning (ML). However, data privacy remains a significant concern. Federated Learning …
Learning (ML). However, data privacy remains a significant concern. Federated Learning …
Spectrum sharing toward delay deterministic wireless network: Delay performance analysis
Z Wei, L Zhang, G Nie, H Wu, N Zhang… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
To accommodate Machine-type Communication (MTC) service, the wireless network needs
to support low-delay and low-jitter data transmission, realizing delay deterministic wireless …
to support low-delay and low-jitter data transmission, realizing delay deterministic wireless …
Federated learning for efficient spectrum allocation in open RAN
In the evolving landscape of Open Radio Access Networks (Open RAN), the dynamic and
unpredictable nature of network conditions presents significant challenges for traditional …
unpredictable nature of network conditions presents significant challenges for traditional …
Incentive-Aware Partitioning and Offloading Scheme for Inference Services in Edge Computing
TY Kim, CK Kim, S Lee, SK Lee - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Owing to remarkable improvements in deep neural networks (DNNs), various computation-
intensive and delay-sensitive DNN services have been developed for smart IoT devices …
intensive and delay-sensitive DNN services have been developed for smart IoT devices …
Challenges in Federated Learning for Resource-Constrained IoT Environments: Energy Efficiency, Privacy, and Statistical Heterogeneity
Due to the extensive Internet availability and interconnections of sensing devices, new
sensor-equipped or Internet-of-Things (IoT) devices with increased processing and …
sensor-equipped or Internet-of-Things (IoT) devices with increased processing and …
MPC-enabled privacy-preserving machine learning
Z Liu - 2023 - dr.ntu.edu.sg
Privacy-Preserving Machine Learning (PPML) has received much attention from the
machine learning community, from academic researchers to industry practitioners to …
machine learning community, from academic researchers to industry practitioners to …