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…
EncCluster: Scalable Functional Encryption in Federated Learning through Weight Clustering and Probabilistic Filters
Vasileios Tsouvalas, Samaneh Mohammadi, Ali Balador +3
Federated Learning (FL) enables model training across decentralized devices by communicating solely local model updates to an aggregation server. Although such limited data sharing…
Communication-Efficient Federated Learning through Adaptive Weight Clustering and Server-Side Distillation
Vasileios Tsouvalas, Aaqib Saeed, Tanir Ozcelebi +1
Federated Learning (FL) is a promising technique for the collaborative training of deep neural networks across multiple devices while preserving data privacy. Despite its potential…
FedCode: Communication-Efficient Federated Learning via Transferring Codebooks
Saeed Khalilian, Vasileios Tsouvalas, Tanir Ozcelebi +1
Federated Learning (FL) is a distributed machine learning paradigm that enables learning models from decentralized local data. While FL offers appealing properties for clients' dat…
Intelligent Blockage Recognition using Cellular mmWave Beamforming Data: Feasibility Study
Bram van Berlo, Yang Miao, Rizqi Hersyandika +4
Joint Communication and Sensing (JCAS) is envisioned for 6G cellular networks, where sensing the operation environment, especially in presence of humans, is as important as the hig…
Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labels
Vasileios Tsouvalas, Aaqib Saeed, Tanir Ozcelebi +1
Federated Learning (FL) is a distributed machine learning paradigm that enables learning models from decentralized private datasets, where the labeling effort is entrusted to the c…
Privacy-preserving Speech Emotion Recognition through Semi-Supervised Federated Learning
Vasileios Tsouvalas, Tanir Ozcelebi, Nirvana Meratnia
Speech Emotion Recognition (SER) refers to the recognition of human emotions from natural speech. If done accurately, it can offer a number of benefits in building human-centered c…
Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks
Bram van Berlo, Amany Elkelany, Tanir Ozcelebi +1
The increasing bandwidth requirement of new wireless applications has lead to standardization of the millimeter wave spectrum for high-speed wireless communication. The millimeter…