Zobrazeno 1 - 10
of 119
pro vyhledávání: '"KHALILI, RAMIN"'
In recent years, Large Language Models (LLMs) through Transformer structures have dominated many machine learning tasks, especially text processing. However, these models require massive amounts of data for training and induce high resource requireme
Externí odkaz:
http://arxiv.org/abs/2411.07826
The Intelligent Transportation System (ITS) environment is known to be dynamic and distributed, where participants (vehicle users, operators, etc.) have multiple, changing and possibly conflicting objectives. Although Reinforcement Learning (RL) algo
Externí odkaz:
http://arxiv.org/abs/2403.08879
Autor:
Koursioumpas, Nikolaos, Magoula, Lina, Stavrakakis, Ioannis, Alonistioti, Nancy, Gutierrez-Estevez, M. A., Khalili, Ramin
Beyond 5G and 6G networks are expected to support new and challenging use cases and applications that depend on a certain level of Quality of Service (QoS) to operate smoothly. Predicting the QoS in a timely manner is of high importance, especially f
Externí odkaz:
http://arxiv.org/abs/2401.10158
Autor:
Koursioumpas, Nikolaos, Magoula, Lina, Petropouleas, Nikolaos, Thanopoulos, Alexandros-Ioannis, Panagea, Theodora, Alonistioti, Nancy, Gutierrez-Estevez, M. A., Khalili, Ramin
Progressing towards a new era of Artificial Intelligence (AI) - enabled wireless networks, concerns regarding the environmental impact of AI have been raised both in industry and academia. Federated Learning (FL) has emerged as a key privacy preservi
Externí odkaz:
http://arxiv.org/abs/2308.10664
Publikováno v:
ACM Comput. Surv. 55, 14s, Article 334, 2023
With an increasing number of smart devices like internet of things (IoT) devices deployed in the field, offloadingtraining of neural networks (NNs) to a central server becomes more and more infeasible. Recent efforts toimprove users' privacy have led
Externí odkaz:
http://arxiv.org/abs/2307.09182
Autor:
Magoula, Lina, Koursioumpas, Nikolaos, Thanopoulos, Alexandros-Ioannis, Panagea, Theodora, Petropouleas, Nikolaos, Gutierrez-Estevez, M. A., Khalili, Ramin
Federated Learning (FL) has emerged as a decentralized technique, where contrary to traditional centralized approaches, devices perform a model training in a collaborative manner, while preserving data privacy. Despite the existing efforts made in FL
Externí odkaz:
http://arxiv.org/abs/2306.14237
Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices
Federated learning (FL) is usually performed on resource-constrained edge devices, e.g., with limited memory for the computation. If the required memory to train a model exceeds this limit, the device will be excluded from the training. This can lead
Externí odkaz:
http://arxiv.org/abs/2305.17005
Autor:
Wiesner, Philipp, Khalili, Ramin, Grinwald, Dennis, Agrawal, Pratik, Thamsen, Lauritz, Kao, Odej
Federated Learning (FL) is an emerging machine learning technique that enables distributed model training across data silos or edge devices without data sharing. Yet, FL inevitably introduces inefficiencies compared to centralized model training, whi
Externí odkaz:
http://arxiv.org/abs/2305.15092
We formulate offloading of computational tasks from a dynamic group of mobile agents (e.g., cars) as decentralized decision making among autonomous agents. We design an interaction mechanism that incentivizes such agents to align private and system g
Externí odkaz:
http://arxiv.org/abs/2208.04237
Performance of vehicle-to-vehicle (V2V) communications depends highly on the employed scheduling approach. While centralized network schedulers offer high V2V communication reliability, their operation is conventionally restricted to areas with full
Externí odkaz:
http://arxiv.org/abs/2207.06537