Energy-Aware Federated Learning With Distributed User Sampling and Multichannel ALOHA
Distributed learning on edge devices has attracted increased attention with the advent of federated learning (FL). Notably, edge devices often have limited […]
On the Primal Feasibility in Dual Decomposition Methods Under Additive and Bounded Errors
With the unprecedented growth of signal processing and machine learning application domains, there has been a tremendous expansion of interest in distributed […]
A Graph Neural Network Learning Approach to Optimize RIS-Assisted Federated Learning
Over-the-air federated learning (FL) is a promising privacy-preserving edge artificial intelligence paradigm, where over-the-air computation enables spectral-efficient model aggregation by achieving simultaneous […]
SlimFL: Federated Learning With Superposition Coding Over Slimmable Neural Networks
Federated learning (FL) is a key enabler for efficient communication and computing, leveraging devices’ distributed computing capabilities. However, applying FL in practice […]
SlimFL
Federated learning (FL) is a key enabler for efficient communication and computing, leveraging devices’ distributed computing capabilities. However, applying FL in practice […]
Optimized Data Sampling and Energy Consumption in IIoT
Real-time environment monitoring is a key application in Industrial Internet of Things where sensors proactively collect and transmit environmental data to the […]
Time-Triggered Federated Learning Over Wireless Networks
The newly emerging federated learning (FL) framework offers a new way to train machine learning models in a privacy-preserving manner. However traditional […]
Deep data plane programming and AI for zero-trust self-driven networking in beyond 5G
Along with the high demand for network connectivity from both end-users and service providers networks have become highly complex; and so has […]
Federated Learning with Correlated Data
While information delivery in industrial Internet of things demands reliability and latency guarantees, the freshness of the controller’s available information, measured by […]
Federated Learning-Based Content Popularity Prediction in Fog Radio Access Networks
In this paper the content popularity prediction problem in fog radio access networks (F-RANs) is investigated. In order to obtain accurate prediction […]