4 citations · 12 across the 26 of their papers we have counts for
26 papers
Ada-TokenCom: Rate-Adaptive Token Communications via Large-Model-Driven Token Compression and Generation
Zijun Zhang, Li Qiao, Mahdi Boloursaz Mashhadi +3
Token Communications (TokenCom) has recently emerged as a new paradigm in which tokens serve as unified units for communication and computation, enabling efficient multimodal seman…
Logic-Driven Semantic Communication for Resilient Multi-Agent Systems
Tamara Alshammari, Mehdi Bennis
The advent of 6G networks is accelerating autonomy and intelligence in large-scale, decentralized multi-agent systems (MAS). While this evolution enables adaptive behavior, it also…
SheafAlign: A Sheaf-theoretic Framework for Decentralized Multimodal Alignment
Abdulmomen Ghalkha, Zhuojun Tian, Chaouki Ben Issaid +1
Conventional multimodal alignment methods assume mutual redundancy across all modalities, an assumption that fails in real-world distributed scenarios. We propose SheafAlign, a she…
Communication-Efficient and Interoperable Distributed Learning
Mounssif Krouka, Mehdi Bennis
Collaborative learning across heterogeneous model architectures presents significant challenges in ensuring interoperability and preserving privacy. We propose a communication-effi…
Resilient-Native and Intelligent Next-Generation Wireless Systems: Key Enablers, Foundations, and Applications
Mehdi Bennis, Sumudu Samarakoon, Tamara Alshammari +3
Just like power, water, and transportation systems, wireless networks are a crucial societal infrastructure. As natural and human-induced disruptions continue to grow, wireless net…
Sheaf-Based Decentralized Multimodal Learning for Next-Generation Wireless Communication Systems
Abdulmomen Ghalkha, Zhuojun Tian, Chaouki Ben Issaid +1
In large-scale communication systems, increasingly complex scenarios require more intelligent collaboration among edge devices collecting various multimodal sensory data to achieve…
Resilient-native and Intelligent NextG Systems
Mehdi Bennis
Just like power, water and transportation systems, wireless networks are a crucial societal infrastructure. As natural and human-induced disruptions continue to grow, wireless netw…
Learning to Collaborate Over Graphs: A Selective Federated Multi-Task Learning Approach
Ahmed Elbakary, Chaouki Ben Issaid, Mehdi Bennis
We present a novel federated multi-task learning method that leverages cross-client similarity to enable personalized learning for each client. To avoid transmitting the entire mod…
Distributionally Robust Federated Learning with Client Drift Minimization
Mounssif Krouka, Chaouki Ben Issaid, Mehdi Bennis
Federated learning (FL) faces critical challenges, particularly in heterogeneous environments where non-independent and identically distributed data across clients can lead to unfa…
Token Communications: A Large Model-Driven Framework for Cross-modal Context-aware Semantic Communications
Li Qiao, Mahdi Boloursaz Mashhadi, Zhen Gao +3
In this paper, we introduce token communications (TokCom), a large model-driven framework to leverage cross-modal context information in generative semantic communications (GenSC).…
Tackling Feature and Sample Heterogeneity in Decentralized Multi-Task Learning: A Sheaf-Theoretic Approach
Chaouki Ben Issaid, Praneeth Vepakomma, Mehdi Bennis
Federated multi-task learning (FMTL) aims to simultaneously learn multiple related tasks across clients without sharing sensitive raw data. However, in the decentralized setting, e…
Sequence Spreading-Based Semantic Communication Under High RF Interference
Hazem Barka, Georges Kaddoum, Mehdi Bennis +2
In the evolving landscape of wireless communications, semantic communication (SemCom) has recently emerged as a 6G enabler that prioritizes the transmission of meaning and contextu…
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…