216 citations · 308 across the 5 of their papers we have counts for
3 papers · 1 filter
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…