Zobrazeno 1 - 10
of 135
pro vyhledávání: '"Parsaeefard, Saeedeh"'
Autor:
Parsaeefard, Saeedeh, Roessel, Sabine, Ghavamabad, Anousheh Gholami, Zaus, Robert, Raaf, Bernhard
User equipment (UE) devices with high compute performance acting on data with dynamic and stochastic nature to train Artificial Intelligence/Machine Learning (AI/ML) models call for real-time, agile distributed machine learning (DL) algorithms. Conse
Externí odkaz:
http://arxiv.org/abs/2409.18268
Autor:
Vaezpour, Elaheh, Majzoobi, Layla, Akbari, Mohammad, Parsaeefard, Saeedeh, Yanikomeroglu, Halim
Cell outage compensation enables a network to react to a catastrophic cell failure quickly and serve users in the outage zone uninterruptedly. Utilizing the promising benefits of non-orthogonal multiple access (NOMA) for improving the throughput of c
Externí odkaz:
http://arxiv.org/abs/2204.03477
This paper studies autonomous and AI-assisted control loops (ACLs) in the next generation of wireless networks in the lens of multi-agent environments. We will study the diverse interactions and conflict management among these loops. We propose "inte
Externí odkaz:
http://arxiv.org/abs/2110.12025
Publikováno v:
Mach. Learn. Knowl. Extr. 2022, 4(2), 397-417
GNNs have been proven to perform highly effective in various node-level, edge-level, and graph-level prediction tasks in several domains. Existing approaches mainly focus on static graphs. However, many graphs change over time with their edge may dis
Externí odkaz:
http://arxiv.org/abs/2108.03400
6G networks will greatly expand the support for data-oriented, autonomous applications for over the top (OTT) and networking use cases. The success of these use cases will depend on the availability of big data sets which is not practical in many rea
Externí odkaz:
http://arxiv.org/abs/2107.05728
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. The Alternating Direction Method of Multipliers (
Externí odkaz:
http://arxiv.org/abs/2104.12608
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on the mixture of experts (MoE) concept to provide robustness in Federated learning (FL) against the poisoned/corrupted/outdated local models. These threats along with the non-IID nature of data sets ca
Externí odkaz:
http://arxiv.org/abs/2104.11700
Publikováno v:
2020 92nd IEEE conference on vehicular technology
Missing data is a challenge in many applications, including intelligent transportation systems (ITS). In this paper, we study traffic speed and travel time estimations in ITS, where portions of the collected data are missing due to sensor instability
Externí odkaz:
http://arxiv.org/abs/2101.03295
Autor:
Moosavi, Reza, Parsaeefard, Saeedeh, Maddah-Ali, Mohammad Ali, Shah-Mansouri, Vahid, Khalaj, Babak Hossein, Bennis, Mehdi
Network function virtualization (NFV) and software defined networking (SDN) are two promising technologies to enable 5G and 6G services and achieve cost reduction, network scalability, and deployment flexibility. However, migration to full SDN/NFV ne
Externí odkaz:
http://arxiv.org/abs/2007.13230