2 citations · 6 across the 13 of their papers we have counts for
13 papers
A Survey of Timing Variability in Microservice-Based Software-Defined Vehicles
Cyrus K. Vattes, Habib Mostafaei, Nirvana Meratnia
Microservice-based systems are modular and adaptable, but their distributed structure makes their timing behavior difficult to analyze and guarantee. Because latency emerges from i…
AutoEncoder-Compressed Parallel Split Learning for Pre-trained Model Fine-Tuning
Bas Meuwissen, Vasileios Tsouvalas, Nirvana Meratnia
Distributed Fine-Tuning (DFT) of large-scale Foundation Models (FMs) on resource-constrained edge devices is limited by local compute constraints and communication overhead. Parall…
AEBNAS: Strengthening Exit Branches in Early-Exit Networks through Hardware-Aware Neural Architecture Search
Oscar Robben, Saeed Khalilian, Nirvana Meratnia
Early-exit networks are effective solutions for reducing the overall energy consumption and latency of deep learning models by adjusting computation based on the complexity of inpu…
EFU: Enforcing Federated Unlearning via Functional Encryption
Samaneh Mohammadi, Vasileios Tsouvalas, Iraklis Symeonidis +4
Federated unlearning (FU) algorithms allow clients in federated settings to exercise their ''right to be forgotten'' by removing the influence of their data from a collaboratively…
Many-Task Federated Fine-Tuning via Unified Task Vectors
Vasileios Tsouvalas, Tanir Ozcelebi, Nirvana Meratnia
Federated Learning (FL) traditionally assumes homogeneous client tasks; however, in real-world scenarios, clients often specialize in diverse tasks, introducing task heterogeneity.…
Fine-tuning Multimodal Transformers on Edge: A Parallel Split Learning Approach
Timo Fudala, Vasileios Tsouvalas, Nirvana Meratnia
Multimodal transformers integrate diverse data types like images, audio, and text, advancing tasks such as audio-visual understanding and image-text retrieval; yet their high param…