Tag: privacy-preserving machine learning

Harvard-led researchers adapted an adult-trained AI model for newborns by training it sequentially on chest x-rays from 30 neonatal intensive ...

A new survey in Cluster Computing unifies blockchain and federated learning into a single framework for securing the Internet of ...

Federated learning has become one of the most actively studied paradigms for collaborative machine learning because it allows model training ...

Researchers have built a federated learning framework for intelligent transportation that combines semi-supervised pseudo-labeling, zk-SNARK update verification and blockchain auditing ...

A new framework combines a platform-free federated learning blockchain consensus mechanism with optimized neural networks to secure electronic health record ...

A new hybrid framework called LLMTransRec combines transformer sequence modeling with large language model embeddings to improve recommendations across sparse ...

An international study of 30 neonatal intensive care units shows that continual learning can adapt adult-trained AI models to newborn ...

Researchers at Jerash University have developed an energy-aware federated deep learning framework that cuts per-round energy use by about 43 ...

A new federated learning framework called FedGPA balances global generalization and client personalization by aggregating neural network layers at different ...

Researchers have developed a privacy-preserving, event-triggered control framework that lets coupled neural networks on signed graphs achieve bipartite synchronization without ...