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Found 3 papers, 2 papers with code
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning
This makes it prone to gradient tampering and privacy leakage by a malicious aggregator.
SCOTCH: An Efficient Secure Computation Framework for Secure Aggregation
Federated learning enables multiple data owners to jointly train a machine learning model without revealing their private datasets.
S++: A Fast and Deployable Secure-Computation Framework for Privacy-Preserving Neural Network Training
In recent times, ReLU has been found to converge much faster and be more computationally efficient as compared to non-linear functions like sigmoid or tanh.
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