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Found 9 papers, 1 papers with code
A Deep Learning-Based Approach for Cell Outage Compensation in NOMA Networks
Cell outage compensation enables a network to react to a catastrophic cell failure quickly and serve users in the outage zone uninterruptedly.
Interaction and Conflict Management in AI-assisted Operational Control Loops in 6G
We propose "interaction and conflict management" (ICM) modules to achieve coherent, consistent and interactions among these ACLs.
Missing Data Estimation in Temporal Multilayer Position-aware Graph Neural Network (TMP-GNN)
GNNs have been proven to perform highly effective in various node-level, edge-level, and graph-level prediction tasks in several domains.
Toward Efficient Transfer Learning in 6G
We also provide a list of future research directions in TL for 6G.
Generalized ADMM in Distributed Learning via Variational Inequality
Due to the explosion in size and complexity of modern data sets and privacy concerns of data holders, it is increasingly important to be able to solve machine learning problems in distributed manners.
Robust Federated Learning by Mixture of Experts
We present a novel weighted average model based on the mixture of experts (MoE) concept to provide robustness in Federated learning (FL) against the poisoned/corrupted/outdated local models.
Estimation of Missing Data in Intelligent Transportation System
We evaluate the effectiveness of this approach on a TomTom dataset containing spatio-temporal measurements of average vehicle speed and travel time in the Greater Toronto Area (GTA).
Representation of Federated Learning via Worst-Case Robust Optimization Theory
Federated learning (FL) is a distributed learning approach where a set of end-user devices participate in the learning process by acting on their isolated local data sets.
Artificial Intelligence as a Services (AI-aaS) on Software-Defined Infrastructure
This paper investigates a paradigm for offering artificial intelligence as a service (AI-aaS) on software-defined infrastructures (SDIs).
