One of the newest terms being used in the world of intelligent technology is federated learning. What is it exactly? Synced brought this interesting topic to our attention in their article, “Dr. Max Welling on Federated Learning and Bayesian Thinking.”
Federated learning is a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging their data samples. Machine learning models, instead of being computed on large, centralized machines, are distributed over mobile devices for computation. It is a new framework for that is distributed over millions of mobile devices enabling mobile phones to collaboratively learn a shared prediction model while keeping all the training data on the device.
Federated learning allows for faster deployment and testing of smarter models, lower latency and less power consumption, all while ensuring privacy.
We will see more of this technology over the next few years as more benefits are realized.
Melody K. Smith
Sponsored by Data Harmony, a unit of Access Innovations, the world leader in indexing and making content findable.