Federated and continual learning for classification tasks in a society of devices

Understand

Today we live in a context in which devices are increasingly interconnected and sensorized and are almost ubiquitous.

• Deep learning has become in recent years a popular way to extract knowledge from the huge amount of data that these devices are able to collect.
• Nevertheless, centralized state-of-the-art learning methods have a number of drawbacks when facing real distributed problems, in which the available information is usually private, partial, biased and evolving over time.
• Federated learning is a popular framework that allows multiple distributed devices to train models remotely, collaboratively, and preserving data privacy.