216 citations · 238 across the 3 of their papers we have counts for
5 papers
A Payload Optimization Method for Federated Recommender Systems
Farwa K. Khan, Adrian Flanagan, Kuan E. Tan +2
We introduce the payload optimization method for federated recommender systems (FRS). In federated learning (FL), the global model payload that is moved between the server and user…
Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions
Alberto Blanco-Justicia, Josep Domingo-Ferrer, Sergio Martínez +3
Federated learning (FL) allows a server to learn a machine learning (ML) model across multiple decentralized clients that privately store their own training data. In contrast with…
A little goes a long way: Improving toxic language classification despite data scarcity
Mika Juuti, Tommi Gröndahl, Adrian Flanagan +1
Detection of some types of toxic language is hampered by extreme scarcity of labeled training data. Data augmentation - generating new synthetic data from a labeled seed dataset -…
Federated Multi-view Matrix Factorization for Personalized Recommendations
Adrian Flanagan, Were Oyomno, Alexander Grigorievskiy +3
We introduce the federated multi-view matrix factorization method that extends the federated learning framework to matrix factorization with multiple data sources. Our method is ab…
Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System
Muhammad Ammad-ud-din, Elena Ivannikova, Suleiman A. Khan +4
The increasing interest in user privacy is leading to new privacy preserving machine learning paradigms. In the Federated Learning paradigm, a master machine learning model is dist…