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Found 12 papers, 7 papers with code
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.
AI/ML-based Load Prediction in IEEE 802.11 Enterprise Networks
Enterprise Wi-Fi networks can greatly benefit from Artificial Intelligence and Machine Learning (AI/ML) thanks to their well-developed management and operation capabilities.
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.
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.
Machine Learning for Performance Prediction of Channel Bonding in Next-Generation IEEE 802.11 WLANs
With the advent of Artificial Intelligence (AI)-empowered communications, industry, academia, and standardization organizations are progressing on the definition of mechanisms and procedures to address the increasing complexity of future 5G and beyond communications.
Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks
Without any doubt, Machine Learning (ML) will be an important driver of future communications due to its foreseen performance when applied to complex problems.
Spatial Reuse in IEEE 802.11ax WLANs
In particular, the main objective of the SR operation is to maximize the utilization of the medium by increasing the number of parallel transmissions.
Networking and Internet Architecture
On the Performance of the Spatial Reuse Operation in IEEE 802.11ax WLANs
The Spatial Reuse (SR) operation included in the IEEE 802. 11ax-2020 (11ax) amendment aims at increasing the number of parallel transmissions in an Overlapping Basic Service Set (OBSS).
Networking and Internet Architecture
Implications of Decentralized Q-learning Resource Allocation in Wireless Networks
Reinforcement Learning is gaining attention by the wireless networking community due to its potential to learn good-performing configurations only from the observed results.
