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20202026
most citedHow to Privately Tune Hyperparameters in Federated Learning? Insights from a Benchmark Study

2 citations · 4 across the 9 of their papers we have counts for

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cs.CR2020

POSEIDON: Privacy-Preserving Federated Neural Network Learning

Sinem Sav, Apostolos Pyrgelis, Juan R. Troncoso-Pastoriza +4

In this paper, we address the problem of privacy-preserving training and evaluation of neural networks in an -party, federated learning setting. We propose a novel system, POSEI…

cs.CR2020

Scalable Privacy-Preserving Distributed Learning

David Froelicher, Juan R. Troncoso-Pastoriza, Apostolos Pyrgelis +4

In this paper, we address the problem of privacy-preserving distributed learning and the evaluation of machine-learning models by analyzing it in the widespread MapReduce abstracti…