Search Results for author:
Found 4 papers, 2 papers with code
Using Confidential Data for Domain Adaptation of Neural Machine Translation
We study the problem of domain adaptation in Neural Machine Translation (NMT) when domain-specific data cannot be shared due to confidentiality or copyright issues.
Privacy-Preserving, Dropout-Resilient Aggregation in Decentralized Learning
Decentralized learning (DL) offers a novel paradigm in machine learning by distributing training across clients without central aggregation, enhancing scalability and efficiency.
Privacy-Preserving Aggregation for Decentralized Learning with Byzantine-Robustness
Decentralized machine learning (DL) has been receiving an increasing interest recently due to the elimination of a single point of failure, present in Federated learning setting.
Privacy-preserving Logistic Regression with Secret Sharing
Research that collects and combines datasets from various data custodians and jurisdictions can excessively benefit from the increased statistical power to support their analyzing goals.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
