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Found 8 papers, 4 papers with code
FedECA: A Federated External Control Arm Method for Causal Inference with Time-To-Event Data in Distributed Settings
External control arms (ECA) can inform the early clinical development of experimental drugs and provide efficacy evidence for regulatory approval in non-randomized settings.
SRATTA : Sample Re-ATTribution Attack of Secure Aggregation in Federated Learning
We consider a cross-silo federated learning (FL) setting where a machine learning model with a fully connected first layer is trained between different clients and a central server using FedAvg, and where the aggregation step can be performed with secure aggregation (SA).
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
In this work, we propose a novel cross-silo dataset suite focused on healthcare, FLamby (Federated Learning AMple Benchmark of Your cross-silo strategies), to bridge the gap between theory and practice of cross-silo FL.
SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning
The Yeo-Johnson (YJ) transformation is a standard parametrized per-feature unidimensional transformation often used to Gaussianize features in machine learning.
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.
Faster RER-CNN: application to the detection of vehicles in aerial images
Detecting small vehicles in aerial images is a difficult job that can be challenging even for humans.
Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks
Object detection-the computer vision task dealing with detecting instances of objects of a certain class (e. g., 'car', 'plane', etc.)
