347 citations · 483 across the 15 of their papers we have counts for
15 papers
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.…
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…
Federated Self-Training for Semi-Supervised Audio Recognition
Vasileios Tsouvalas, Aaqib Saeed, Tanir Ozcelebi
Federated Learning is a distributed machine learning paradigm dealing with decentralized and personal datasets. Since data reside on devices like smartphones and virtual assistants…
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…
Federated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligence
Aaqib Saeed, Flora D. Salim, Tanir Ozcelebi +1
Smartphones, wearables, and Internet of Things (IoT) devices produce a wealth of data that cannot be accumulated in a centralized repository for learning supervised models due to p…
Multi-task Self-Supervised Learning for Human Activity Detection
Aaqib Saeed, Tanir Ozcelebi, Johan Lukkien
Deep learning methods are successfully used in applications pertaining to ubiquitous computing, health, and well-being. Specifically, the area of human activity recognition (HAR) i…
Learning behavioral context recognition with multi-stream temporal convolutional networks
Aaqib Saeed, Tanir Ozcelebi, Stojan Trajanovski +1
Smart devices of everyday use (such as smartphones and wearables) are increasingly integrated with sensors that provide immense amounts of information about a person's daily life s…
Proactive Dependability Framework for Smart Environment Applications
Ehsan Ullah Warriach, Tanir Ozcelebi, Johan J. Lukkien
Smart environment applications demand novel solutions for managing quality of services, especially availability and reliability at run-time. The underlying systems are changing dyn…
Relevance As a Metric for Evaluating Machine Learning Algorithms
Aravind Kota Gopalakrishna, Tanir Ozcelebi, Antonio Liotta +1
In machine learning, the choice of a learning algorithm that is suitable for the application domain is critical. The performance metric used to compare different algorithms must al…