Tag: privacy-preserving machine learning

Mount Sinai researchers built a privacy-preserving, on-device forecasting system that uses simple linear models and Fitbit data to predict sedentary ...

Researchers at Shanghai Polytechnic University have developed CDAFIM, a class-difficulty-aware incentive mechanism that uses Shapley values to fairly reward federated ...

Researchers in Morocco have integrated federated learning with the OneM2M IoT service layer, showing faster, privacy-preserving machine learning at the ...

Researchers have unveiled a collaborative encryption framework that trains deep learning models on sensitive data up to three times faster ...

Researchers have developed a vertical federated deep learning framework using a binary manta ray optimization algorithm that selects cancer-related genes ...

Researchers have developed a differentially private stochastic gradient descent algorithm for outcome weighted learning that learns personalized treatment rules from ...

A new reinforcement learning framework called FLASH-DRM selects the most valuable devices for each federated learning round, improving accuracy, latency ...

A new federated learning framework lets hospitals train chest X-ray diagnostic AI collaboratively without sharing patient data, and it outperforms ...

Researchers have built a federated learning system that combines CKKS homomorphic encryption with blockchain storage, cutting adversarial attack success from ...

Researchers have developed GRAB-FL, a graph-aware federated learning framework that uses graph neural networks to assign trust scores to client ...