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Found 5 papers, 2 papers with code
Differentially Private Federated Learning for Cancer Prediction
Since 2014, the NIH funded iDASH (integrating Data for Analysis, Anonymization, SHaring) National Center for Biomedical Computing has hosted yearly competitions on the topic of private computing for genomic data.
Siloed Federated Learning for Multi-Centric Histopathology Datasets
While federated learning is a promising approach for training deep learning models over distributed sensitive datasets, it presents new challenges for machine learning, especially when applied in the medical domain where multi-centric data heterogeneity is common.
Efficient Sparse Secure Aggregation for Federated Learning
Federated Learning enables one to jointly train a machine learning model across distributed clients holding sensitive datasets.
Federated Survival Analysis with Discrete-Time Cox Models
Building machine learning models from decentralized datasets located in different centers with federated learning (FL) is a promising approach to circumvent local data scarcity while preserving privacy.
Kymatio: Scattering Transforms in Python
The wavelet scattering transform is an invariant signal representation suitable for many signal processing and machine learning applications.
