Decentralized Data

Decentralized data management focuses on collaboratively training machine learning models across multiple data sources without directly sharing sensitive information. Current research emphasizes federated learning, employing various algorithms like federated averaging and more sophisticated approaches addressing data heterogeneity and noise, often incorporating techniques from Bayesian networks, gradient boosting, and even pre-trained language models. This field is crucial for addressing privacy concerns in diverse applications, from healthcare and finance to IoT networks, enabling powerful machine learning while respecting data ownership and security.