Federated Learning - Unlocking the Value of Discrete Data for Enterprise

Explore the concept of federated learning and its potential to unlock the value of discrete data for enterprises in this 42-minute talk by Henry Zhang and Layne Peng from VMware. Discover how this collaborative machine learning technique allows multiple clients to train algorithms without sharing raw data, enabling organizations to break data silos and mine valuable information while complying with privacy protection laws. Learn about the widely used open-source frameworks FATE and OpenFL, and their applications in industries such as finance and healthcare. Delve into the challenges of orchestrating federated learning in production environments and gain insights into the FedLCM (Federation Lifecycle Manager) project, which offers a unified experience for managing different federated learning frameworks in multi-cloud settings. Gain valuable knowledge about federated learning principles, typical use cases, and the orchestration challenges faced in implementing this technology across organizations.