4 citations · 12 across the 26 of their papers we have counts for
12 papers · 1 filter
Communicate Less, Synthesize the Rest: Latency-aware Intent-based Generative Semantic Multicasting with Diffusion Models
Xinkai Liu, Mahdi Boloursaz Mashhadi, Li Qiao +3
Generative diffusion models (GDMs) have recently shown great success in synthesizing multimedia signals with high perceptual quality, enabling highly efficient semantic communicati…
MIRA: A Method of Federated MultI-Task Learning for LaRge LAnguage Models
Ahmed Elbakary, Chaouki Ben Issaid, Tamer ElBatt +2
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 ea…
Scalable and Resource-Efficient Second-Order Federated Learning via Over-the-Air Aggregation
Abdulmomen Ghalkha, Chaouki Ben Issaid, Mehdi Bennis
Second-order federated learning (FL) algorithms offer faster convergence than their first-order counterparts by leveraging curvature information. However, they are hindered by high…
Generative Semantic Communication via Textual Prompts: Latency Performance Tradeoffs
Mengmeng Ren, Li Qiao, Long Yang +6
This paper develops an edge-device collaborative Generative Semantic Communications (Gen SemCom) framework leveraging pre-trained Multi-modal/Vision Language Models (M/VLMs) for ul…
Five Key Enablers for Communication during and after Disasters
Mohammad Shehab, Mustafa Kishk, Maurilio Matracia +2
Civilian communication during disasters such as earthquakes, floods, and military conflicts is crucial for saving lives. Nevertheless, several challenges exist during these circums…
A Web-Based Solution for Federated Learning with LLM-Based Automation
Chamith Mawela, Chaouki Ben Issaid, Mehdi Bennis
Federated Learning (FL) offers a promising approach for collaborative machine learning across distributed devices. However, its adoption is hindered by the complexity of building r…
Toward Explainable Reasoning in 6G: A Proof of Concept Study on Radio Resource Allocation
Farhad Rezazadeh, Sergio Barrachina-Muñoz, Hatim Chergui +5
The move toward artificial intelligence (AI)-native sixth-generation (6G) networks has put more emphasis on the importance of explainability and trustworthiness in network manageme…
An Internal Model Principle For Robots
Vadim K. Weinstein, Tamara Alshammari, Kalle G. Timperi +2
When designing a robot's internal system, one often makes assumptions about the structure of the intended environment of the robot. One may even assign meaning to various internal…
Fed-Sophia: A Communication-Efficient Second-Order Federated Learning Algorithm
Ahmed Elbakary, Chaouki Ben Issaid, Mohammad Shehab +3
Federated learning is a machine learning approach where multiple devices collaboratively learn with the help of a parameter server by sharing only their local updates. While gradie…
Meta Reinforcement Learning for Resource Allocation in Aerial Active-RIS-assisted Networks with Rate-Splitting Multiple Access
Sajad Faramarzi, Sepideh Javadi, Farshad Zeinali +5
Mounting a reconfigurable intelligent surface (RIS) on an unmanned aerial vehicle (UAV) holds promise for improving traditional terrestrial network performance. Unlike conventional…
Latency-Aware Generative Semantic Communications with Pre-Trained Diffusion Models
Li Qiao, Mahdi Boloursaz Mashhadi, Zhen Gao +3
Generative foundation AI models have recently shown great success in synthesizing natural signals with high perceptual quality using only textual prompts and conditioning signals t…
Resource Allocation in STAR-RIS-Aided SWIPT with RSMA via Meta-Learning
Mojtaba Amiri, Elaheh Vaezpour, Sepideh Javadi +3
Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is a cutting-edge concept for the sixth-generation (6G) wireless networks. In this paper, w…