4 citations · 12 across the 26 of their papers we have counts for
10 papers · 1 filter
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…