Federated Learning


Privacy used to be so common (in the 1990s and early 2000s) that you literally could not escape it. Interactive advances in technology, social media not being the least of them, has rapidly eroded privacy to the point where it is one of the leading motivating factors in strengthening information security. Privacy is now viewed as a luxury and laws have slowly expanded into the field of privacy to accommodate users who are increasingly seeing their privacy violated. 

Machine learning has responded to the demand for a return to privacy by birthing a new method of data analysis: federated learning. 

This article will provide a high-level exploration of this emerging method of machine learning by examining what federated learning is, how federated learning works, characteristics of federated learning, application, and differential privacy.

What is federated learning?

Previous methods of machine learning algorithms are intended for highly controlled environments (data centers for example) with balanced data distributed between a high number of machines where high speed and capacity networks are available. Data used for machine learning is stored on either a central server or the cloud. This comes with a high price tag, not to mention user data privacy concerns. 

Federated learning is a family of algorithms that are an alternative method of machine learning. It helps to solve these privacy and cost considerations by using a centralized server to coordinate a federation of participating devices. 

How does federated learning work?

This central server provides the model for participating devices but most of the learning work is performed by the federated users themselves, including training the model itself. There are different forms of federated learning, but they all have the following in common — a central server coordinates federated devices, or nodes, and initiates models for (Read more...)

*** This is a Security Bloggers Network syndicated blog from Infosec Resources authored by Greg Belding. Read the original post at: