Zobrazeno 1 - 10
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pro vyhledávání: '"A. Bennis"'
Generative diffusion models (GDMs) have recently shown great success in synthesizing multimedia signals with high perceptual quality enabling highly efficient semantic communications in future wireless networks. In this paper, we develop an intent-aw
Externí odkaz:
http://arxiv.org/abs/2411.02334
Our future society will be increasingly digitalised, hyper-connected and globally data driven. The sixth generation (6G) and beyond 6G wireless networks are expected to bridge the digital and physical worlds by providing wireless connectivity as a se
Externí odkaz:
http://arxiv.org/abs/2410.23203
In this paper, we introduce a method for fine-tuning Large Language Models (LLMs), inspired by Multi-Task learning in a federated manner. Our approach leverages the structure of each client's model and enables a learning scheme that considers other c
Externí odkaz:
http://arxiv.org/abs/2410.15524
Second-order federated learning (FL) algorithms offer faster convergence than their first-order counterparts by leveraging curvature information. However, they are hindered by high computational and storage costs, particularly for large-scale models.
Externí odkaz:
http://arxiv.org/abs/2410.07662
This letter introduces a machine-learning approach to learning the semantic dynamics of correlated systems with different control rules and dynamics. By leveraging the Koopman operator in an autoencoder (AE) framework, the system's state evolution is
Externí odkaz:
http://arxiv.org/abs/2410.02303
Autor:
Bennis, Driss, Bouziri, Ayoub
Let R be a commutative ring, and let S be a multiplicative subset of R. In this paper, we introduce and investigate the notion of S-FP-injective modules. Among other results, we show that, under certain conditions, a ring R is S-Noetherian if and onl
Externí odkaz:
http://arxiv.org/abs/2410.00167
In this work, we propose a novel data-driven machine learning (ML) technique to model and predict the dynamics of the wireless propagation environment in latent space. Leveraging the idea of channel charting, which learns compressed representations o
Externí odkaz:
http://arxiv.org/abs/2409.10045
Autor:
Ren, Mengmeng, Qiao, Li, Yang, Long, Gao, Zhen, Chen, Jian, Mashhadi, Mahdi Boloursaz, Xiao, Pei, Tafazolli, Rahim, Bennis, Mehdi
This paper develops an edge-device collaborative Generative Semantic Communications (Gen SemCom) framework leveraging pre-trained Multi-modal/Vision Language Models (M/VLMs) for ultra-low-rate semantic communication via textual prompts. The proposed
Externí odkaz:
http://arxiv.org/abs/2409.09715
Civilian communication during disasters such as earthquakes, floods, and military conflicts is crucial for saving lives. Nevertheless, several challenges exist during these circumstances such as the destruction of cellular communication and electrici
Externí odkaz:
http://arxiv.org/abs/2409.06822
Federated Learning (FL) offers a promising approach for collaborative machine learning across distributed devices. However, its adoption is hindered by the complexity of building reliable communication architectures and the need for expertise in both
Externí odkaz:
http://arxiv.org/abs/2408.13010