4 citations · 9 across the 12 of their papers we have counts for
12 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…
Large-Scale AI in Telecom: Charting the Roadmap for Innovation, Scalability, and Enhanced Digital Experiences
Adnan Shahid, Adrian Kliks, Ahmed Al-Tahmeesschi +132
This white paper discusses the role of large-scale AI in the telecommunications industry, with a specific focus on the potential of generative AI to revolutionize network functions…
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…
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…
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…
DR-DSGD: A Distributionally Robust Decentralized Learning Algorithm over Graphs
Chaouki Ben Issaid, Anis Elgabli, Mehdi Bennis
In this paper, we propose to solve a regularized distributionally robust learning problem in the decentralized setting, taking into account the data distribution shift. By adding a…
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning
Anis Elgabli, Chaouki Ben Issaid, Amrit S. Bedi +3
Newton-type methods are popular in federated learning due to their fast convergence. Still, they suffer from two main issues, namely: low communication efficiency and low privacy d…