Federated Data

Federated data focuses on collaboratively training machine learning models across multiple decentralized datasets without directly sharing the raw data, thereby preserving privacy. Current research emphasizes efficient data handling techniques like deep transfer hashing and sparse sampling for streaming data, as well as the development of privacy-preserving algorithms such as differential privacy and secure multi-party computation, often applied to various model architectures including neural networks and tree-based models like XGBoost. This approach is crucial for addressing data silos in sensitive domains like healthcare and finance, enabling collaborative model training while adhering to stringent privacy regulations and fostering responsible AI development.