[HTML][HTML] Achieving security and privacy in federated learning systems: Survey, research challenges and future directions
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 …
multiple decentralized clients that privately store their own training data. In contrast with …
[PDF][PDF] Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions
A Blanco-Justiciaa, J Domingo-Ferrera, S Martínezab… - crises-deim.urv.cat
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 …
multiple decentralized clients that privately store their own training data. In contrast with …
Achieving security and privacy in federated learning systems:: Survey, research challenges and future directions
A Blanco-Justicia, J Domingo-Ferrer, S Martínez… - 2021 - dl.acm.org
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 …
multiple decentralized clients that privately store their own training data. In contrast with …
Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions
A Blanco-Justicia, J Domingo-Ferrer… - arXiv e …, 2020 - ui.adsabs.harvard.edu
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 …
multiple decentralized clients that privately store their own training data. In contrast with …
Achieving security and privacy in federated learning systems: Survey, research challenges and future directions
A Blanco Justicia, J Domingo Ferrer… - Engineering …, 2021 - openaccess.uoc.edu
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 …
multiple decentralized clients that privately store their own training data. In contrast with …
Achieving security and privacy in federated learning systems: Survey, research challenges and future directions
A Blanco-Justicia, J Domingo-Ferrer, S Martínez… - repositori.urv.cat
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 …
multiple decentralized clients that privately store their own training data. In contrast with …
Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions
A Blanco-Justicia, J Domingo-Ferrer, S Martínez… - arXiv preprint arXiv …, 2020 - arxiv.org
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 …
multiple decentralized clients that privately store their own training data. In contrast with …