Zobrazeno 1 - 10
of 193
pro vyhledávání: '"Sorour, Sameh"'
Mobile Edge Learning (MEL) is a learning paradigm that enables distributed training of Machine Learning models over heterogeneous edge devices (e.g., IoT devices). Multi-orchestrator MEL refers to the coexistence of multiple learning tasks with diffe
Externí odkaz:
http://arxiv.org/abs/2109.12409
Mobile Edge Learning (MEL) is a collaborative learning paradigm that features distributed training of Machine Learning (ML) models over edge devices (e.g., IoT devices). In MEL, possible coexistence of multiple learning tasks with different datasets
Externí odkaz:
http://arxiv.org/abs/2109.00757
This paper extends the paradigm of "mobile edge learning (MEL)" by designing an optimal task allocation scheme for training a machine learning model in an asynchronous manner across mutiple edge nodes or learners connected via a resource-constrained
Externí odkaz:
http://arxiv.org/abs/2012.00143
Machine learning (ML) and artificial intelligence (AI) have recently made a significant impact on improving the operations of wireless networks and establishing intelligence at the edge. In return, rare efforts were made to explore how adapting, opti
Externí odkaz:
http://arxiv.org/abs/2006.07453
This paper proposes to maximize the accuracy of a distributed machine learning (ML) model trained on learners connected via the resource-constrained wireless edge. We jointly optimize the number of local/global updates and the task size allocation to
Externí odkaz:
http://arxiv.org/abs/2006.07402
Autor:
Mohammad, Umair, Sorour, Sameh
This paper proposes a scheme to efficiently execute distributed learning tasks in an asynchronous manner while minimizing the gradient staleness on wireless edge nodes with heterogeneous computing and communication capacities. The approach considered
Externí odkaz:
http://arxiv.org/abs/1905.01656
Publikováno v:
In Ad Hoc Networks 1 April 2023 142
Autor:
Mohammad, Umair, Sorour, Sameh
This paper aims to establish a new optimization paradigm for implementing realistic distributed learning algorithms, with performance guarantees, on wireless edge nodes with heterogeneous computing and communication capacities. We will refer to this
Externí odkaz:
http://arxiv.org/abs/1811.03748
One of the most promising techniques for network-wide interference management necessitates a redesign of the network architecture known as cloud radio access network (CRAN). The cloud is responsible for coordinating multiple Remote Radio Heads (RRHs)
Externí odkaz:
http://arxiv.org/abs/1806.08230
Despite the significant advances in vehicle automation and electrification, the next-decade aspirations for massive deployments of autonomous electric mobility on demand (AEMoD) services are still threatened by two major bottlenecks, namely the compu
Externí odkaz:
http://arxiv.org/abs/1804.11328