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Found 9 papers, 1 papers with code
Quantized Hierarchical Federated Learning: A Robust Approach to Statistical Heterogeneity
This paper presents a novel hierarchical federated learning algorithm within multiple sets that incorporates quantization for communication-efficiency and demonstrates resilience to statistical heterogeneity.
Hierarchical Over-the-Air Federated Learning with Awareness of Interference and Data Heterogeneity
When implementing hierarchical federated learning over wireless networks, scalability assurance and the ability to handle both interference and device data heterogeneity are crucial.
Optimal Receive Filter Design for Misaligned Over-the-Air Computation
The fundamental idea of OAC is to exploit signal superposition to compute functions of multiple simultaneously transmitted signals.
Resource Dimensioning for Single-Cell Edge Video Analytics
Edge intelligence is an emerging technology where the base stations located at the edge of the network are equipped with computing units that provide machine learning services to the end users.
Scalable Hierarchical Over-the-Air Federated Learning
When implementing hierarchical federated learning over wireless networks, scalability assurance and the ability to handle both interference and device data heterogeneity are crucial.
Modelling multi-cell edge video analytics
Edge intelligence is a scalable solution for analyzing distributed data, but it cannot provide reliable services in large-scale cellular networks unless the inherent aspects of fading and interference are also taken into consideration.
Over-the-Air Federated Learning with Retransmissions (Extended Version)
Finally, we propose a heuristic for selecting the optimal number of retransmissions, which can be calculated before training the ML model.
Wireless for Machine Learning
As data generation increasingly takes place on devices without a wired connection, machine learning (ML) related traffic will be ubiquitous in wireless networks.
Distributed Algorithms for Feature Extraction Off-loading in Multi-Camera Visual Sensor Networks
Real-time visual analysis tasks, like tracking and recognition, require swift execution of computationally intensive algorithms.
