55 citations · 149 across the 14 of their papers we have counts for
16 papers
Privacy-preserving Chunk Scheduling in a BitTorrent Implementation of Federated Learning
Naicheng Li, Javad Dogani, Rui Wang +2
Traditional federated learning (FL) relies on a central aggregator server, which can create performance bottlenecks and privacy risks. Decentralized mix-and-forward designs remove…
FedQV: Leveraging Quadratic Voting in Federated Learning
Tianyue Chu, Nikolaos Laoutaris
Federated Learning (FL) permits different parties to collaboratively train a global model without disclosing their respective local labels. A crucial step of FL, that of aggregatin…
PriPrune: Quantifying and Preserving Privacy in Pruned Federated Learning
Tianyue Chu, Mengwei Yang, Nikolaos Laoutaris +1
Federated learning (FL) is a paradigm that allows several client devices and a server to collaboratively train a global model, by exchanging only model updates, without the devices…
Securing Federated Sensitive Topic Classification against Poisoning Attacks
Tianyue Chu, Alvaro Garcia-Recuero, Costas Iordanou +2
We present a Federated Learning (FL) based solution for building a distributed classifier capable of detecting URLs containing GDPR-sensitive content related to categories such as…
A Unified Graph-Based Approach to Disinformation Detection using Contextual and Semantic Relations
Marius Paraschiv, Nikos Salamanos, Costas Iordanou +2
As recent events have demonstrated, disinformation spread through social networks can have dire political, economic and social consequences. Detecting disinformation must inevitabl…
Beyond content analysis: Detecting targeted ads via distributed counting
Costas Iordanou, Nicolas Kourtellis, Juan Miguel Carrascosa +3
Being able to check whether an online advertisement has been targeted is essential for resolving privacy controversies and implementing in practice data protection regulations like…