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Found 13 papers, 4 papers with code
Mobile Traffic Prediction at the Edge through Distributed and Transfer Learning
This translates to a considerable amount of energy for data transmission and processing.
The Implications of Decentralization in Blockchained Federated Learning: Evaluating the Impact of Model Staleness and Inconsistencies
Blockchain promises to enhance distributed machine learning (ML) approaches such as federated learning (FL) by providing further decentralization, security, immutability, and trust, which are key properties for enabling collaborative intelligence in next-generation applications.
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.
The Cost of Training Machine Learning Models over Distributed Data Sources
Federated learning is one of the most appealing alternatives to the standard centralized learning paradigm, allowing a heterogeneous set of devices to train a machine learning model without sharing their raw data.
To Compute or not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing
We consider a network of smart sensors for an edge computing application that sample a time-varying signal and send updates to a base station for remote global monitoring.
On the Decentralization of Blockchain-enabled Asynchronous Federated Learning
Federated learning (FL), thanks in part to the emergence of the edge computing paradigm, is expected to enable true real-time applications in production environments.
Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANs
As wireless standards evolve, more complex functionalities are introduced to address the increasing requirements in terms of throughput, latency, security, and efficiency.
Analysis and Evaluation of Synchronous and Asynchronous FLchain
As our results show, the synchronous setting leads to higher prediction accuracy than the asynchronous case.
Balancing the Payment System
We also briefly introduce a generalization of a payment system and of the method to balance it in the form of a specific application (Tetris Core Technologies), whose wider adoption could contribute to the financial stability of and better management of liquidity and risk for the whole economy.
Distributed Deep Reinforcement Learning for Functional Split Control in Energy Harvesting Virtualized Small Cells
We formulate the corresponding grid energy and traffic drop rate minimization problem, and propose a distributed deep reinforcement learning (DDRL) solution.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Recurrent Neural Networks for Handover Management in Next-Generation Self-Organized Networks
In this paper, we discuss a handover management scheme for Next Generation Self-Organized Networks.
Mobile Traffic Classification through Physical Channel Fingerprinting: a Deep Learning Approach
The automatic classification of applications and services is an invaluable feature for new generation mobile networks.
Dynamic Control of Functional Splits for Energy Harvesting Virtual Small Cells: a Distributed Reinforcement Learning Approach
In this paper, we propose a network scenario where the baseband processes of the virtual small cells powered solely by energy harvesters and batteries can be opportunistically executed in a grid-connected edge computing server, co-located at the macro base station site.
Systems and Control Systems and Control
