Summary

In this chapter, we learned about a privacy preservation mechanism for ML known as federated learning. In traditional ML, all data is aggregated and processed in a central location, but in FML, the data remains distributed across multiple devices or locations, and the model is trained in a decentralized manner. In FML, we share the model and not the data.

We discussed the core concepts and working of FML, followed by an implementation in Python. We also benchmarked the performance of federated learning against traditional ML approaches to examine the privacy-utility trade-off. This chapter provided an introduction to an important aspect of ML and one that is gaining rapid traction in today’s privacy-centric technology world.

In the next chapter, we will go a step further and look at the hottest topic in ML privacy today – differential privacy.