9 citations · 17 across the 26 of their papers we have counts for
6 papers · 1 filter
Is Our Benchmark Enough? An Analysis of Continual Learning for MLLMs
Van-Tuan Tran, Shruthi Gowda, Merim Dzaferagic +1
Continual adaptation is essential for multimodal large language models (MLLMs) deployed across evolving domains, but the state-of-the-art MR-LoRA method highly relies on the assump…
UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models
Van-Tuan Tran, Hong-Hanh Nguyen-Le, Marco Ruffini +1
Heterogeneous LoRA-rank methods address system heterogeneity in federated fine-tuning of foundation models by assigning client-specific ranks based on computational capabilities. H…
msData: A Millisecond-Resolution Network Dataset for Advancing Time Series Foundation Models
Subina Khanal, Seshu Tirupathi, Merim Dzaferagic +2
Time series foundation models (TSFMs) require diverse, real-world datasets to adapt across varying domains and temporal frequencies. However, current large-scale datasets predomina…
Dynamic Features Adaptation in Networking: Toward Flexible training and Explainable inference
Yannis Belkhiter, Seshu Tirupathi, Giulio Zizzo +2
As AI becomes a native component of 6G network control, AI models must adapt to continuously changing conditions, including the introduction of new features and measurements driven…
ML Approach for Power Consumption Prediction in Virtualized Base Stations
Merim Dzaferagic, Jose A. Ayala-Romero, Marco Ruffini
The flexibility introduced with the Open Radio Access Network (O-RAN) architecture allows us to think beyond static configurations in all parts of the network. This paper addresses…
Near Real-Time Distributed State Estimation via AI/ML-Empowered 5G Networks
Ognjen Kundacina, Miodrag Forcan, Mirsad Cosovic +5
Fifth-Generation (5G) networks have a potential to accelerate power system transition to a flexible, softwarized, data-driven, and intelligent grid. With their evolving support for…