Search Results for author:
Found 7 papers, 3 papers with code
Intelligent Client Selection for Federated Learning using Cellular Automata
Federated Learning (FL) has emerged as a promising solution for privacy-enhancement and latency minimization in various real-world applications, such as transportation, communications, and healthcare.
Towards Energy-Aware Federated Traffic Prediction for Cellular Networks
Cellular traffic prediction is a crucial activity for optimizing networks in fifth-generation (5G) networks and beyond, as accurate forecasting is essential for intelligent network design, resource allocation and anomaly mitigation.
Federated Learning for Early Dropout Prediction on Healthy Ageing Applications
In this paper, we present a federated machine learning (FML) approach that minimizes privacy concerns and enables distributed training, without transferring individual data.
Predicting Early Dropouts of an Active and Healthy Ageing App
The presented algorithms have been submitted to the IFMBE Scientific Challenge 2022, part of IUPESM WC 2022.
Federated Learning for 5G Base Station Traffic Forecasting
In this work, we investigate the efficacy of federated learning applied to raw base station LTE data for time-series forecasting.
FedPOIRec: Privacy Preserving Federated POI Recommendation with Social Influence
In this work, we present FedPOIRec, a privacy preserving federated learning approach enhanced with features from users' social circles for top-$N$ POI recommendations.
Federated Neural Collaborative Filtering
The system, named FedNCF, enables learning without requiring users to disclose or transmit their raw data.
