11 papers
Semantic-based Distributed Learning for Diverse and Discriminative Representations
Zhuojun Tian, Chaouki Ben Issaid, Mehdi Bennis
In large-scale distributed scenarios, increasingly complex tasks demand more intelligent collaboration across networks, requiring the joint extraction of structural representations…
Communication-Efficient and Robust Multi-Modal Federated Learning via Latent-Space Consensus
Mohamed Badi, Chaouki Ben Issaid, Mehdi Bennis
Federated learning (FL) enables collaborative model training across distributed devices without sharing raw data, but applying FL to multi-modal settings introduces significant cha…
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…
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…
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…