Gaining Insights from Private Data Using Federated Learning

Sid Roy, Manager of Machine Learning Engineering at Devron, explains the concept of federated learning - a machine learning approach that enables training models on inaccessible data while preserving privacy. This technology is particularly useful for situations where companies want to access certain data but cannot due to privacy, regulatory, or jurisdictional reasons. Devron's platform allows data scientists to build, train, and evaluate machine learning models without ever having access to the data, making it a valuable tool in industries with strict privacy regulations. Roy also discusses the potential for federated learning applications in academia and how this technology can mitigate bias in models by improving the variety of data fed into them.