Blockchain-Based Privacy Preserving Deep Learning

53 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Incentive Design for Efficient Federated Learning in Mobile Networks: A Contract Theory Approach (2019, background): “This causes not only large computation and storage overhead, but also serious risk of privacy breach due to the centralized entity suffering from single point of failure [3].”
Towards Fair and Privacy-Preserving Federated Deep Models (2019, background): “[15], [16] investigated privacy-preserving deep learning on blockchain.”
Incentive Mechanism for Reliable Federated Learning: A Joint Optimization Approach to Combining Reputation and Contract Theory (2019, background): “This causes excessive computation and storage cost, and the mobile devices also suffer from serious privacy leakage risk [2].”
Reliable Federated Learning for Mobile Networks (2019, background): “This results in exorbitant communication and storage cost, and the mobile users are at risk of serious privacy leakage [3].”