2 citations · 5 across the 12 of their papers we have counts for
12 papers
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…
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…
Federated Fine-Tuning of Foundation Models via Probabilistic Masking
Vasileios Tsouvalas, Yuki Asano, Aaqib Saeed
Foundation Models (FMs) have revolutionized machine learning with their adaptability and high performance across tasks; yet, their integration into Federated Learning (FL) is chall…
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…
Plug-and-Play Multilingual Few-shot Spoken Words Recognition
Aaqib Saeed, Vasileios Tsouvalas
As technology advances and digital devices become prevalent, seamless human-machine communication is increasingly gaining significance. The growing adoption of mobile, wearable, an…
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…